text:
@inproceedings{conf/icip/ChenNT11,
author = {0001, Yi Chen and Nasrabadi, Nasser M and Tran, Trac D},
booktitle = {ICIP},
editor = {Macq, Benoit and Schelkens, Peter},
isbn = {978-1-4577-1304-0},
keywords = {dblp},
pages = {1233--1236},
publisher = {IEEE},
title = {{Hyperspectral image classification via kernel sparse representation.}},
url = {http://dblp.uni-trier.de/db/conf/icip/icip2011.html{\#}ChenNT11},
year = {2011}
}
@book{russell2003artificial,
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  year={2003},
  publisher={Prentice hall Upper Saddle River}
}
@article{domingos2012few,
  title={A few useful things to know about machine learning},
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  year={2012},
  publisher={ACM}
}
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  title={A logical calculus of the ideas immanent in nervous activity},
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  title={On the algorithmic implementation of multiclass kernel-based vector machines},
  author={Crammer, Koby and Singer, Yoram},
  journal={Journal of machine learning research},
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  title={Application of majority voting to pattern recognition: an analysis of its behavior and performance},
  author={Lam, Louisa and Suen, SY},
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@article{ruta2005classifier,
  title={Classifier selection for majority voting},
  author={Ruta, Dymitr and Gabrys, Bogdan},
  journal={Information fusion},
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  number={1},
  pages={63--81},
  year={2005},
  publisher={Elsevier}
}
@inproceedings{czarnecki2015maximum,
  title={Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation},
  author={Czarnecki, Wojciech Marian and Jozefowicz, Rafal and Tabor, Jacek},
  booktitle={Joint European Conference on Machine Learning and Knowledge Discovery in Databases},
  pages={52--67},
  year={2015},
  organization={Springer}
}
@article{bengio2015deep,
  title={Deep learning},
  author={Bengio, Yoshua and Goodfellow, Ian J and Courville, Aaron},
  journal={An MIT Press book in preparation. Draft chapters available at http://www. iro. umontreal. ca/~ bengioy/dlbook},
  year={2015},
  publisher={Citeseer}
}
@inproceedings{cordella2000cascaded,
  title={A cascaded multiple expert system for verification},
  author={Cordella, Luigi P and Foggia, Pasquale and Sansone, Carlo and Tortorella, Francesco and Vento, Mario},
  booktitle={International Workshop on Multiple Classifier Systems},
  pages={330--339},
  year={2000},
  organization={Springer}
}
@misc{wozniak2006metody,
 author={Woźniak, Michał},
 type={książka},
 year=2006,
 publisher={Oficyna Wydawnicza Politechniki Wrocławskiej},
 title={Metody fuzji informacji dla komputerowych systemów rozpoznawania},
 language={pol},
 keywords={eksploracja danych, decyzje (podejmowanie), fuzje danych},
}
@book{kuncheva2004combining,
  title={Combining pattern classifiers: methods and algorithms},
  author={Kuncheva, Ludmila I},
  year={2004},
  publisher={John Wiley \& Sons}
}
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  title={Estimation of dependences based on empirical data},
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}
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  title={Escape from freedom},
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  publisher={Macmillan}
}
@book{fraser2004real,
  title={Real world color management},
  author={Fraser, Bruce and Murphy, Chris and Bunting, Fred},
  year={2004},
  publisher={Pearson Education}
}
@article{wansink2007mindless,
  title={Mindless eating the 200 daily food decisions we overlook},
  author={Wansink, Brian and Sobal, Jeffery},
  journal={Environment and Behavior},
  volume={39},
  number={1},
  pages={106--123},
  year={2007},
  publisher={Sage Publications}
}
@article{journals/tgrs/LiBP10,
author = {0009, Jun Li and Bioucas-Dias, Jos{\'{e}} M and Plaza, Antonio J},
journal = {IEEE T. Geoscience and Remote Sensing},
keywords = {dblp},
number = {11},
pages = {4085--4098},
title = {{Semisupervised Hyperspectral Image Segmentation Using Multinomial Logistic Regression With Active Learning.}},
url = {http://dblp.uni-trier.de/db/journals/tgrs/tgrs48.html{\#}LiBP10},
volume = {48},
year = {2010}
}
@book{wozniak2013hybrid,
  title={Hybrid classifiers: methods of data, knowledge, and classifier combination},
  author={Wozniak, Michal},
  volume={519},
  year={2013},
  publisher={Springer}
}
@inproceedings{agarwal_efficient_2007,
abstract = {Hyperspectral systems have improved significantly through recent advancements in sensor technology, which have made possible to acquire data with several hundred channels. These advances provide the possible benefit of not only collecting more detailed information than previously possible, but also of producing more accurate data. Some of the major challenges in handling such large data sets are removing redundant information and assuring the continued relevance of vital information to the application at hand. For example, conventional methods for land use and land cover classifications may not be applicable, due to the large data volumes used to characterize hyperspectral cubes. Therefore, these conventional methods may require a preprocessing step, namely dimension reduction. Dimension reduction can be seen as a transformation from a high order dimension to a low order dimension in order to conquer the so- called "curse of the dimensionality," which eliminates data redundancy. Principal Component Analysis ({\{}PCA){\}} is one such data reduction technique, which is often used when analyzing remotely sensed data. In computing the principal components, the eigenvalues of the covariance matrix of the 3-D image must be computed. Since this is a global operation it requires high computational resources and requires whole image to be stored which increases memory requirements. This paper reports an hierarchical algorithm, which can effectively reduce the hyperspectral data to intrinsic dimensionality. In the hierarchical {\{}PCA{\}}, we break the image into various parts and then perform {\{}PCA{\}} on each part separately and then combine the results. The classification results over the original and the resulting reduced data have been compared. The results show that reduced data obtained by hierarchical {\{}PCA{\}} can compare favorably to the results obtained from original data.},
author = {Agarwal, A and El-Ghazawi, T and El-Askary, H and Le-Moigne, J},
booktitle = {2007 {\{}IEEE{\}} International Symposium on Signal Processing and Information Technology},
doi = {10.1109/ISSPIT.2007.4458191},
keywords = {Hyperspectral,Hyperspectral imaging,Hyperspectral sensors,Image sensors,Instruments,Layout,Principal component analysis,Remote sensing,Signal processing,Space technology,Spectroscopy,covariance matrices,covariance matrix,data reduction,data redundancy,dimension reduction,eigenvalue,eigenvalues and eigenfunctions,geophysical signal processing,hyperspectral imagery,image classification,image segmentation,spectral analysis,{\{}PCA{\}}},
pages = {353--356},
title = {{Efficient Hierarchical-{\{}PCA{\}} Dimension Reduction for Hyperspectral Imagery}},
year = {2007}
}
@article{makarau2012,
author = {{Aliaksei Makarau Rupert M¨uller}, Gintautas Palubinskas and Reinartz, Peter},
journal = {International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences},
title = {{Hyperspectral data classification using factor graphs}},
year = {2012}
}
@article{Alpaydin:1999,
author = {Alpaydin, Ethem},
journal = {Neural Computation},
number = {8},
pages = {1885--1892},
title = {{Combined 5 x 2 cv F Test for Comparing Supervised Classification Learning Algorithms}},
volume = {11},
year = {1999}
}
@misc{asuncion2007uci,
author = {Asuncion, Arthur and Newman, David},
title = {{UCI machine learning repository}},
year = {2007}
}
@incollection{bagheri_2012,
author = {Bagheri, MohammadAli and Gao, Qigang and Escalera, Sergio},
booktitle = {Advances in Artificial Intelligence},
doi = {10.1007/978-3-642-30353-1_3},
editor = {Kosseim, Leila and Inkpen, Diana},
isbn = {978-3-642-30352-4},
keywords = {Multiclass; Pairwise classification; Neural Networ},
pages = {25--36},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
title = {{Efficient Pairwise Classification Using Local Cross Off Strategy}},
url = {http://dx.doi.org/10.1007/978-3-642-30353-1{\_}3},
volume = {7310},
year = {2012}
}
@article{Bengio2012,
archivePrefix = {arXiv},
arxivId = {1206.5538},
author = {Bengio, Yoshua and Courville, Aaron and Vincent, Pascal},
doi = {10.1109/TPAMI.2013.50},
eprint = {1206.5538},
file = {:Users/xehivs/Library/Application Support/Mendeley Desktop/Downloaded/Bengio, Courville, Vincent - 2012 - Representation Learning A Review and New Perspectives.pdf:pdf},
isbn = {0162-8828 VO - 35},
issn = {1939-3539},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
number = {8},
pages = {1798--1828},
pmid = {23787338},
title = {{Representation Learning: A Review and New Perspectives}},
volume = {35},
year = {2012}
}
@article{Bengio:2004:NUE:1005332.1044695,
author = {Bengio, Yoshua and Grandvalet, Yves},
issn = {1532-4435},
journal = {J. Mach. Learn. Res.},
pages = {1089--1105},
publisher = {JMLR.org},
title = {{No Unbiased Estimator of the Variance of K-Fold Cross-Validation}},
url = {http://dl.acm.org/citation.cfm?id=1005332.1044695},
volume = {5},
year = {2004}
}
@inproceedings{bennett1999semi,
author = {Bennett, Kristin P and Demiriz, Ayhan and Others},
booktitle = {Advances in Neural Information Processing Systems},
pages = {368--374},
publisher = {MIT Press},
title = {{Semi-supervised support vector machines}},
year = {1998}
}
@incollection{KNIME,
author = {Berthold, MichaelR. and Cebron, Nicolas and Dill, Fabian and Gabriel, ThomasR. and K{\"{o}}tter, Tobias and Meinl, Thorsten and Ohl, Peter and Sieb, Christoph and Thiel, Kilian and Wiswedel, Bernd},
booktitle = {Data Analysis, Machine Learning and Applications},
doi = {10.1007/978-3-540-78246-9_38},
editor = {Preisach, Christine and Burkhardt, Hans and Schmidt-Thieme, Lars and Decker, Reinhold},
isbn = {978-3-540-78239-1},
pages = {319--326},
publisher = {Springer Berlin Heidelberg},
series = {Studies in Classification, Data Analysis, and Knowledge Organization},
title = {{KNIME: The Konstanz Information Miner}},
url = {http://dx.doi.org/10.1007/978-3-540-78246-9{\_}38},
year = {2008}
}
@article{cai_hyperspectral_2007,
abstract = {Displaying the abundant information contained in a hyperspectral image is a challenging problem. Almost any visualization approach reduces the information content. However, we want to maximize the amount of object or material information presented. A visualization approach that uses classification as an intermediate step may maximize the information transfer. In our research, we are particularly interested in the display of mixed-pixel classification results, since most pixels in a remotely sensed hyperspectral image are mixed pixels. In this paper, we propose a visualization technique that employs two layers to integrate the mixture information (i.e., endmembers and their abundances) in each pixel. Images can be displayed with any desired level of details.},
author = {Cai, Shangshu and Du, Qian and Moorhead, R J},
doi = {10.1109/TGRS.2007.894922},
issn = {0196-2892},
journal = {{\{}IEEE{\}} Transactions on Geoscience and Remote Sensing},
keywords = {Colored noise,Displays,Hyperspectral sensors,Image analysis,Personal communication networks,Pixel,Student members,double layers,geophysical techniques,hyperspectral imagery visualization,hyperspectral imaging,image classification,image processing,linear unmixing analysis,material information,mixed-pixel classification,object amount maximization,principal component analysis,remote sensing,unsupervised classification,visualization},
number = {10},
pages = {3028--3036},
title = {{Hyperspectral Imagery Visualization Using Double Layers}},
volume = {45},
year = {2007}
}
@article{A-CeaWasBenChaFauSve10,
author = {Ceamanos, Xavier and Waske, Bj{\"{o}}rn and Benediktsson, J{\'{o}}n Atli and Chanussot, Jocelyn and Fauvel, Mathieu and Sveinsson, Johannes R},
journal = {Int. J. Image Graphics},
number = {4},
pages = {293--307},
title = {{A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data}},
volume = {1},
year = {2010}
}
@article{journals/tgrs/ChiB07,
author = {Chi, Mingmin and Bruzzone, Lorenzo},
journal = {IEEE T. Geoscience and Remote Sensing},
keywords = {dblp},
number = {6-2},
pages = {1870--1880},
title = {{Semisupervised Classification of Hyperspectral Images by SVMs Optimized in the Primal.}},
url = {http://dblp.uni-trier.de/db/journals/tgrs/tgrs45.html{\#}ChiB07},
volume = {45},
year = {2007}
}
@article{chi2009ensemble,
author = {Chi, Mingmin and Kun, Qian and Benediktsson, J{\'{o}}n Atli and Feng, Rui},
journal = {IEEE GEOSCIENCE AND REMOTE SENSING LETTERS},
number = {4},
title = {{Ensemble Classification Algorithm for Hyperspectral Remote Sensing Data}},
volume = {6},
year = {2009}
}
@inproceedings{conf/igarss/ChiQB08,
author = {Chi, Mingmin and Qian, Qun and Benediktsson, Jon Atli},
booktitle = {IGARSS (1)},
keywords = {dblp},
pages = {209--212},
publisher = {IEEE},
title = {{Cluster-Based Ensemble Classification for Hyperspectral Remote Sensing Images.}},
url = {http://dblp.uni-trier.de/db/conf/igarss/igarss2008-1.html{\#}ChiQB08},
year = {2008}
}
@article{cui_interactive_2009,
abstract = {In this paper, we propose a new framework to visualize hyperspectral images. We present three goals for such a visualization: 1) preservation of spectral distances; 2) discriminability of pixels with different spectral signatures; 3) and interactive visualization for analysis. The introduced method considers all three goals at the same time and produces higher quality output than existing methods. The technical contribution of our mapping is to derive a simplified convex optimization from a complex nonlinear optimization problem. During interactive visualization, we can map the spectral signature of pixels to red, green, and blue colors using a combination of principal component analysis and linear programming. In the results, we present a quantitative analysis to demonstrate the favorable attributes of our algorithm.},
author = {Cui, Ming and Razdan, A and Hu, Jiuxiang and Wonka, P},
doi = {10.1109/TGRS.2008.2010129},
issn = {0196-2892},
journal = {{\{}IEEE{\}} Transactions on Geoscience and Remote Sensing},
keywords = {Hyperspectral image visualization,convex optimization,convex programming,data visualisation,geophysical signal processing,geophysical techniques,image colour analysis,interactive hyperspectral image visualization,interactive systems,linear programming,nonlinear optimization problem,perceptual color distances,pixel spectral signature discriminability,principal component analysis,principal component analysis ({\{}PCA){\}},remote sensing,spectral distances},
number = {6},
pages = {1673--1684},
title = {{Interactive Hyperspectral Image Visualization Using Convex Optimization}},
volume = {47},
year = {2009}
}
@article{4814563,
author = {Cui, Ming and Razdan, A and Hu, Jiuxiang and Wonka, P},
doi = {10.1109/TGRS.2008.2010129},
issn = {0196-2892},
journal = {Geoscience and Remote Sensing, IEEE Transactions on},
keywords = {convex programming;data visualisation;geophysical},
number = {6},
pages = {1673--1684},
title = {{Interactive Hyperspectral Image Visualization Using Convex Optimization}},
volume = {47},
year = {2009}
}
@article{Czarnecki2015,
abstract = {Representation learning is currently a very hot topic in modern machine learning, mostly due to the great success of the deep learning methods. In particular low-dimensional representation which discriminates classes can not only enhance the classification procedure, but also make it faster, while contrary to the high-dimensional embeddings can be efficiently used for visual based exploratory data analysis. In this paper we propose Maximum Entropy Linear Manifold (MELM), a multidimensional generalization of Multithreshold Entropy Linear Classifier model which is able to find a low-dimensional linear data projection maximizing discriminativeness of projected classes. As a result we obtain a linear embedding which can be used for classification, class aware dimensionality reduction and data visualization. MELM provides highly discriminative 2D projections of the data which can be used as a method for constructing robust classifiers. We provide both empirical evaluation as well as some interesting theoretical properties of our objective function such us scale and affine transformation invariance, connections with PCA and bounding of the expected balanced accuracy error.},
archivePrefix = {arXiv},
arxivId = {1504.02622},
author = {Czarnecki, Wojciech Marian and J{\'{o}}zefowicz, Rafa{\l} and Tabor, Jacek},
doi = {10.1007/978-3-319-23528-8_4},
eprint = {1504.02622},
isbn = {978-3-319-23528-8; 978-3-319-23527-1},
title = {{Maximum Entropy Linear Manifold for Learning Discriminative Low-dimensional Representation}},
url = {http://arxiv.org/abs/1504.02622},
year = {2015}
}
@book{davies_machine_2004,
abstract = {In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this {\{}need.As{\}} in earlier editions, {\{}E.R.{\}} Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems.{\textperiodcentered} Includes solid, accessible coverage of 2-D and 3-D scene analysis.{\textperiodcentered} Offers thorough treatment of the Hough Transform—a key technique for inspection and surveillance.{\textperiodcentered} Brings vital topics and techniques together in an integrated system design approach.{\textperiodcentered} Takes full account of the requirement for real-time processing in real applications.},
author = {Davies, E R},
isbn = {9780080473246},
keywords = {Business {\&} Economics / Industrial Management,Computers / Computer Science,Computers / Data Processing,Computers / Intelligence ({\{}AI){\}} {\&} Semantics,Technology {\&} Engineering / Industrial Health {\&} Sa},
publisher = {Elsevier},
shorttitle = {Machine Vision},
title = {{Machine Vision: Theory, Algorithms, Practicalities}},
year = {2004}
}
@book{davies_machine_2004,
abstract = {In the last 40 years, machine vision has evolved into a mature field embracing a wide range of applications including surveillance, automated inspection, robot assembly, vehicle guidance, traffic monitoring and control, signature verification, biometric measurement, and analysis of remotely sensed images. While researchers and industry specialists continue to document their work in this area, it has become increasingly difficult for professionals and graduate students to understand the essential theory and practicalities well enough to design their own algorithms and systems. This book directly addresses this {\{}need.As{\}} in earlier editions, {\{}E.R.{\}} Davies clearly and systematically presents the basic concepts of the field in highly accessible prose and images, covering essential elements of the theory while emphasizing algorithmic and practical design constraints. In this thoroughly updated edition, he divides the material into horizontal levels of a complete machine vision system. Application case studies demonstrate specific techniques and illustrate key constraints for designing real-world machine vision systems.{\textperiodcentered} Includes solid, accessible coverage of 2-D and 3-D scene analysis.{\textperiodcentered} Offers thorough treatment of the Hough Transform—a key technique for inspection and surveillance.{\textperiodcentered} Brings vital topics and techniques together in an integrated system design approach.{\textperiodcentered} Takes full account of the requirement for real-time processing in real applications.},
author = {Davies, E R},
isbn = {9780080473246},
keywords = {Business {\&} Economics / Industrial Management,Computers / Computer Science,Computers / Data Processing,Computers / Intelligence ({\{}AI){\}} {\&} Semantics,Technology {\&} Engineering / Industrial Health {\&} Sa},
publisher = {Elsevier},
shorttitle = {Machine Vision},
title = {{Machine Vision: Theory, Algorithms, Practicalities}},
year = {2004}
}
@article{DBLP:journals/corr/cs-AI-9501101,
author = {Dietterich, Thomas G and Bakiri, Ghulum},
journal = {CoRR},
title = {{Solving Multiclass Learning Problems via Error-Correcting Output Codes}},
volume = {cs.AI/9501},
year = {1995}
}
@inproceedings{DBLP:conf/igarss/Dopido0PB12,
author = {Dopido, Inmaculada and Li, Jun and Plaza, Antonio and Bioucas-Dias, Jos{\'{e}} M},
booktitle = {IGARSS},
pages = {1586--1589},
title = {{Semi-supervised active learning for urban hyperspectral image classification}},
year = {2012}
}
@article{4524015,
author = {Du, Qian and Raksuntorn, N and Cai, Shangshu and Moorhead, R J},
doi = {10.1109/TGRS.2008.916203},
issn = {0196-2892},
journal = {Geoscience and Remote Sensing, IEEE Transactions on},
keywords = {colour displays;data visualisation;geophysical sig},
number = {6},
pages = {1858--1866},
title = {{Color Display for Hyperspectral Imagery}},
volume = {46},
year = {2008}
}
@article{durand_improved_1989,
abstract = {A technique to enhance multispectral image displays and to improve their visual interpretations is proposed. The decorrelation approach is tested with a {\{}SPOT{\}} simulation and thematic mapper image. The technique is considered to be of great help for the interpretation of remotely sensed images. Furthermore, the choice between two-, three-, and N -dimensional decorrelation allows the user to adjust to specific cases. However, the main drawback of this technique is its strong link to the statistics of the image being displayed, which makes the analysis of the displayed image data (in terms of spectral properties of the surface, for example) more complex. For a specific application conducted with a given satellite, this inconvenience could be overcome by using a set of fixed linear combinations},
author = {Durand, J M and Kerr, Y H},
doi = {10.1109/TGRS.1989.35944},
issn = {0196-2892},
journal = {{\{}IEEE{\}} Transactions on Geoscience and Remote Sensing},
keywords = {Color,Colored noise,Crops,Decorrelation,Displays,Multispectral imaging,Remote sensing,Signal synthesis,Signal to noise ratio,Vegetation mapping,correlation methods,decorrelation method,geophysical techniques,image displays,multispectral data,picture processing,remotely sensed images,statistics,technique,thematic mapper image,visual interpretations,{\{}SPOT{\}} simulation},
number = {5},
pages = {611--619},
title = {{An improved decorrelation method for the efficient display of multispectral data}},
volume = {27},
year = {1989}
}
@article{Escalera:2010:EOC:1756006.1756026,
author = {Escalera, Sergio and Pujol, Oriol and Radeva, Petia},
issn = {1532-4435},
journal = {J. Mach. Learn. Res.},
pages = {661--664},
publisher = {JMLR.org},
title = {{Error-Correcting Ouput Codes Library}},
url = {http://dl.acm.org/citation.cfm?id=1756006.1756026},
volume = {11},
year = {2010}
}
@article{mango,
author = {Finkel, Raphael and Bentley, J L},
journal = {Acta Informatica},
title = {{Quad Trees: A Data Structure for Retrieval on Composite Keys}},
year = {1974}
}
@article{1703909,
author = {Giakos, G C},
doi = {10.1109/TIM.2006.881030},
issn = {0018-9456},
journal = {IEEE Transactions on Instrumentation and Measurement},
keywords = {image sensors;optical images;photon polarisation;p},
number = {5},
pages = {1628--1633},
title = {{Multifusion, Multispectral, Optical Polarimetric Imaging Sensing Principles}},
volume = {55},
year = {2006}
}
@article{Goodfellow201559,
abstract = {Abstract The {\{}ICML{\}} 2013 Workshop on Challenges in Representation Learning11 http://deeplearning.net/icml2013-workshop-competition. focused on three challenges: the black box learning challenge, the facial expression recognition challenge, and the multimodal learning challenge. We describe the datasets created for these challenges and summarize the results of the competitions. We provide suggestions for organizers of future challenges and some comments on what kind of knowledge can be gained from machine learning competitions. },
annote = {Special Issue on “Deep Learning of Representations”},
author = {Goodfellow, Ian J and Erhan, Dumitru and Carrier, Pierre Luc and Courville, Aaron and Mirza, Mehdi and Hamner, Ben and Cukierski, Will and Tang, Yichuan and Thaler, David and Lee, Dong-Hyun and Zhou, Yingbo and Ramaiah, Chetan and Feng, Fangxiang and Li, Ruifan and Wang, Xiaojie and Athanasakis, Dimitris and Shawe-Taylor, John and Milakov, Maxim and Park, John and Ionescu, Radu and Popescu, Marius and Grozea, Cristian and Bergstra, James and Xie, Jingjing and Romaszko, Lukasz and Xu, Bing and Chuang, Zhang and Bengio, Yoshua},
doi = {http://dx.doi.org/10.1016/j.neunet.2014.09.005},
issn = {0893-6080},
journal = {Neural Networks},
keywords = {Competition,Dataset,Representation learning},
pages = {59--63},
title = {{Challenges in representation learning: A report on three machine learning contests}},
url = {http://www.sciencedirect.com/science/article/pii/S0893608014002159},
volume = {64},
year = {2015}
}
@article{GRA2012,
abstract = {We propose a specific content-based image retrieval (CBIR) system for hyperspectral images exploiting its rich spectral information. The CBIR image features are the endmember signatures obtained from the image data by endmember induction algorithms (EIAs). Endmembers correspond to the elementary materials in the scene, so that the pixel spectra can be decomposed into a linear combination of endmember signatures. EIA search for points in the high dimensional space of pixel spectra defining a convex polytope, often a simplex, covering the image data. This paper introduces a dissimilarity measure between hyperspectral images computed over the image induced endmembers, proving that it complies with the axioms of a distance. We provide a comparative discussion of dissimilarity functions, and quantitative evaluation of their relative performances on a large collection of synthetic hyperspectral images, and on a dataset extracted from a real hyperspectral image. Alternative dissimilarity functions considered are the Hausdorff distance and robust variations of it. We assess the CBIR performance sensitivity to changes in the distance between endmembers, the EIA employed, and some other conditions. The proposed hyperspectral image distance improves over the alternative dissimilarities in all quantitative performance measures. The visual results of the CBIR on the real image data demonstrate its usefulness for practical applications.},
author = {Gra{\~{n}}a, Manuel and Veganzones, Miguel A. and {Grana M.}, Veganzones M A},
doi = {10.1016/j.patcog.2012.03.015},
issn = {00313203},
journal = {Pattern Recognition},
keywords = {Content based image retrieval,Endmember induction algorithm,Hyperspectral images,Image similarity},
month = {sep},
number = {45(9)},
pages = {3472--3489},
title = {{An endmember-based distance for content based hyperspectral image retrieval}},
url = {http://www.sciencedirect.com/science/article/pii/S0031320312001483},
volume = {45},
year = {2012}
}
@article{green_imaging_1998,
abstract = {Imaging spectroscopy is of growing interest as a new approach to Earth remote sensing. The Airborne {\{}Visible/Infrared{\}} Imaging Spectrometer ({\{}AVIRIS){\}} was the first imaging sensor to measure the solar reflected spectrum from 400 nm to 2500 nm at 10 nm intervals. The calibration accuracy and signal-to-noise of {\{}AVIRIS{\}} remain unique. The {\{}AVIRIS{\}} system as well as the science research and applications have evolved significantly in recent years. The initial design and upgraded characteristics of the {\{}AVIRIS{\}} system are described in terms of the sensor, calibration, data system, and flight operation. This update on the characteristics of {\{}AVIRIS{\}} provides the context for the science research and applications that use {\{}AVIRIS{\}} data acquired in the past several years. Recent science research and applications are reviewed spanning investigations of atmospheric correction, ecology and vegetation, geology and soils, inland and coastal waters, the atmosphere, snow and ice hydrology, biomass burning, environmental hazards, satellite simulation and calibration, commercial applications, spectral algorithms, human infrastructure, as well as spectral modeling.},
author = {Green, Robert O and Eastwood, Michael L and Sarture, Charles M and Chrien, Thomas G and Aronsson, Mikael and Chippendale, Bruce J and Faust, Jessica A and Pavri, Betina E and Chovit, Christopher J and Solis, Manuel and Olah, Martin R and Williams, Orlesa},
doi = {10.1016/S0034-4257(98)00064-9},
issn = {0034-4257},
journal = {Remote Sensing of Environment},
number = {3},
pages = {227--248},
title = {{Imaging Spectroscopy and the Airborne {\{}Visible/Infrared{\}} Imaging Spectrometer ({\{}AVIRIS){\}}}},
url = {http://www.sciencedirect.com/science/article/pii/S0034425798000649},
volume = {65},
year = {1998}
}
@article{green_imaging_1998,
abstract = {Imaging spectroscopy is of growing interest as a new approach to Earth remote sensing. The Airborne {\{}Visible/Infrared{\}} Imaging Spectrometer ({\{}AVIRIS){\}} was the first imaging sensor to measure the solar reflected spectrum from 400 nm to 2500 nm at 10 nm intervals. The calibration accuracy and signal-to-noise of {\{}AVIRIS{\}} remain unique. The {\{}AVIRIS{\}} system as well as the science research and applications have evolved significantly in recent years. The initial design and upgraded characteristics of the {\{}AVIRIS{\}} system are described in terms of the sensor, calibration, data system, and flight operation. This update on the characteristics of {\{}AVIRIS{\}} provides the context for the science research and applications that use {\{}AVIRIS{\}} data acquired in the past several years. Recent science research and applications are reviewed spanning investigations of atmospheric correction, ecology and vegetation, geology and soils, inland and coastal waters, the atmosphere, snow and ice hydrology, biomass burning, environmental hazards, satellite simulation and calibration, commercial applications, spectral algorithms, human infrastructure, as well as spectral modeling.},
author = {Green, Robert O and Eastwood, Michael L and Sarture, Charles M and Chrien, Thomas G and Aronsson, Mikael and Chippendale, Bruce J and Faust, Jessica A and Pavri, Betina E and Chovit, Christopher J and Solis, Manuel and Olah, Martin R and Williams, Orlesa},
doi = {10.1016/S0034-4257(98)00064-9},
issn = {0034-4257},
journal = {Remote Sensing of Environment},
number = {3},
pages = {227--248},
title = {{Imaging Spectroscopy and the Airborne {\{}Visible/Infrared{\}} Imaging Spectrometer ({\{}AVIRIS){\}}}},
url = {http://www.sciencedirect.com/science/article/pii/S0034425798000649},
volume = {65},
year = {1998}
}
@book{history_przewodnik_????,
author = {for History, Center and Media, New},
title = {{Przewodnik {\{}"Szybki{\}} start"}},
url = {http://zotero.org/support/quick{\_}start{\_}guide}
}
@incollection{Jackowski:2010,
author = {Jackowski, Konrad},
booktitle = {Hybrid Artificial Intelligence Systems},
doi = {10.1007/978-3-642-13769-3_66},
editor = {{Gra{\~{n}}a Romay}, Manuel and Corchado, Emilio and {Garcia Sebastian}, M.Teresa},
isbn = {978-3-642-13768-6},
keywords = {pattern recognition; classifier fusion; multiple c},
pages = {540--547},
publisher = {Springer Berlin Heidelberg},
series = {Lecture Notes in Computer Science},
title = {{Multiple Classifier System with Radial Basis Weight Function}},
url = {http://dx.doi.org/10.1007/978-3-642-13769-3{\_}66},
volume = {6076},
year = {2010}
}
@inproceedings{Jackowski2014,
author = {Jackowski, Konrad and Jankowski, Dariusz and Ksieniewicz, Pawe{\l} and Simi{\'{c}}, Dragan and Simi{\'{c}}, Svetlana and Wo{\'{z}}niak, Micha{\l}},
booktitle = {Information Technologies in Biomedicine, Volume 4},
doi = {10.1007/978-3-319-06596-0_25},
file = {:Users/xehivs/Articles/2014{\_}itib/main.pdf:pdf},
pages = {273--284},
title = {{Ensemble Classifier Systems for Headache Diagnosis}},
year = {2014}
}
@article{jacobson_design_2005,
abstract = {Design goals and solutions are proposed for the display of hyperspectral imagery on tristimulus displays. The requirements of a hyperspectral visualization depend on the task. We focus on creating consistent representations of hyperspectral data that can facilitate understanding and analysis of hyperspectral scenes, and may be used in conjunction with task-specific visualizations. Fixed linear spectral weighting envelopes are given, creating natural-looking imagery where hue, brightness, saturation, and whitepoint have meanings consistent with the human visual system interpretation of natural scenes. For Airborne {\{}Visible/Infrared{\}} Imaging Spectrometer images, hue interpretation of water and vegetation is also preserved. The proposed designs avoid the preattentive distractions of principal component analysis imagery, and appear to provide comparable or enhanced spectral and edge discriminability.},
author = {Jacobson, N P and Gupta, M R},
doi = {10.1109/TGRS.2005.857623},
issn = {0196-2892},
journal = {{\{}IEEE{\}} Transactions on Geoscience and Remote Sensing},
keywords = {Airborne {\{}Visible/Infrared{\}} Imaging Spectrometer,Brightness,Data visualization,Displays,Humans,Infrared imaging,Infrared spectra,Layout,Spectroscopy,Visual system,data visualisation,dimensionality reduction,edge discriminability,geophysical techniques,hyperspectral imaging,image fusion,image processing,multidimensional signal processing,natural scenes,principal component analysis,principal components analysis,principal components analysis ({\{}PCA){\}},remote sensing,spectral discriminability,task-specific visualization,visualization},
number = {11},
pages = {2684--2692},
title = {{Design goals and solutions for display of hyperspectral images}},
volume = {43},
year = {2005}
}
@article{4305369,
author = {Jacobson, N P and Gupta, M R and Cole, J B},
doi = {10.1109/TGRS.2007.903598},
issn = {0196-2892},
journal = {Geoscience and Remote Sensing, IEEE Transactions on},
keywords = {data visualisation;geophysics computing;image fusi},
number = {10},
pages = {3277--3288},
title = {{Linear Fusion of Image Sets for Display}},
volume = {45},
year = {2007}
}
@techreport{jessica2005seeing,
author = {Jessica, M},
institution = {ISBN 0-534-39402-7},
title = {{Seeing Through Statistics 3rd Edition, Thomson Brooks/Cole, 2005}},
year = {2005}
}
@inproceedings{kong1995error,
author = {Kong, Eun Bae and Dietterich, Thomas G},
booktitle = {ICML},
pages = {313--321},
title = {{Error-Correcting Output Coding Corrects Bias and Variance.}},
year = {1995}
}
@article{6168802,
author = {Kotwal, K and Chaudhuri, S},
doi = {10.1109/JSTARS.2012.2187274},
issn = {1939-1404},
journal = {Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of},
keywords = {data visualisation;geophysical image processing;im},
number = {2},
pages = {501--509},
title = {{An Optimization-Based Approach to Fusion of Hyperspectral Images}},
volume = {5},
year = {2012}
}
@article{5395694,
author = {Kotwal, K and Chaudhuri, S},
doi = {10.1109/TGRS.2009.2037950},
issn = {0196-2892},
journal = {Geoscience and Remote Sensing, IEEE Transactions on},
keywords = {geophysical image processing;geophysical technique},
number = {5},
pages = {2308--2316},
title = {{Visualization of Hyperspectral Images Using Bilateral Filtering}},
volume = {48},
year = {2010}
}
@article{Krawczyk:2014,
author = {Krawczyk, B and Filipczuk, P},
journal = {Engineering Applications of Artificial Intelligence},
pages = {126--135},
title = {{Cytological image analysis with firefly nuclei detection and hybrid one-class classification decomposition}},
volume = {31},
year = {2014}
}
@inproceedings{Krawczyk2014a,
abstract = {{\textcopyright} Springer International Publishing Switzerland 2014. Hyperspectral image analysis is among one of the current trends in computer vision and machine learning. Due to the high dimensionality, large number of classes, presence of noise and complex structure, this is not a trivial task. There exists a need for more precise and computationally efficient algorithms for hyperspectral image segmentation and classification. In this paper, we introduce a novel algorithm for segmentation of hyperspectral images and selecting valuable pixels for classifier training procedure. Our approach is based on Quad Tree Decomposition method, which detects homogeneous region in the input image. This allows to precisely detect groups with similar structure and recognize different possible classes on the image. We discuss the computational complexity of our algorithm and show that it can be easily used in real-life applications. Further, this algorithm is extended by active learning approach, which allows to select a single representative pixel for each detected homogeneous region. With this, the classifier is trained on a significantly reduced dataset without sacrificing its quality.We examine the correlation between the number of folds taken by our segmentation algorithm and used classifiers. We show, that the segmentation procedure can be stopped earlier without drop of the accuracy.},
author = {Krawczyk, Bartosz and Ksieniewicz, Pawe{\l} and Wo{\'{z}}niak, Micha{\l}},
booktitle = {Advances in Intelligent Systems and Computing},
doi = {10.1007/978-3-319-07995-0_11},
file = {:Users/xehivs/Articles/2014{\_}soco/main.pdf:pdf},
isbn = {9783319079943},
issn = {21945357},
keywords = {Hyperspectral image,Image segmentation,Machine learning,Pattern recognition},
pages = {105--113},
title = {{Hyperspectral image analysis based on quad tree decomposition}},
volume = {299},
year = {2014}
}
@inproceedings{Krawczyk2014,
abstract = {Hyperspectral image analysis is a dynamically developing branch of computer vision due to the numerous practical applications and high complexity of data. There exist a need for introducing novel machine learning methods, that can tackle high dimensionality and large number of classes in these images. In this paper, we introduce a novel ensemble method for classification of hyperspectral data. The pool of classifiers is built on the basis of color decomposition of the given image. Each base classifier corresponds to a single color channel that is extracted. We propose a new method for decomposing hyperspectral image into 11 different color channels. As not all of the channels may bear as useful information as other, we need to promote the most relevant ones. For this, our ensemble uses a weighted trained fuser, which uses a neural methods for establishing weights. We show, that the proposed ensemble can outperform other state-of-the-art classifiers in the given task. {\textcopyright} 2014 Springer International Publishing.},
author = {Krawczyk, Bartosz and Ksieniewicz, Pawe{\l} and Wo{\'{z}}niak, Micha{\l}},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-07617-1_25},
file = {:Users/xehivs/Articles/2014{\_}hais/main.pdf:pdf},
isbn = {9783319076164},
issn = {16113349 03029743},
keywords = {classifier ensemble,color channels,hyperspectral image,image segmentation,machine learning,multiple classifier system},
pages = {274--284},
title = {{Hyperspectral image analysis based on color channels and ensemble classifier}},
volume = {8480 LNAI},
year = {2014}
}
@inproceedings{Ksieniewicz2015,
author = {Ksieniewicz, Pawe{\l} and Gra{\~{n}}a, Manuel and Wo{\'{z}}niak, Micha{\l}},
booktitle = {Computational Collective Intelligence},
doi = {10.1007/978-3-319-24306-1_56},
file = {:Users/xehivs/Articles/2015{\_}iccci/main.pdf:pdf},
isbn = {978-3-319-24306-1; 978-3-319-24305-4},
pages = {578--587},
series = {Lecture Notes in Artificial Intelligence},
title = {{Blurred Labeling Segmentation Algorithm for Hyperspectral Images}},
url = {{\textless}Go to ISI{\textgreater}://WOS:000366123600056},
volume = {9330},
year = {2015}
}
@article{Ksieniewicz2014,
abstract = {Nowadays, the hyperspectral imaging is the focus of intense research, because its applications can be very useful in the natural disaster monitoring and agricultural monitoring to enumerate only a few. The main problem of systems using hyperspectral imaging is the cost of labelling, because it requires the domain experts, who label the region or prepare the labelled learning set for machine learning methods. The article presents a novel Hyperspectral Segmentation Algorithm which is a part of a general framework used for image classification. The algorithm is based on an image decomposition into homogeneous regions using a novel similarity measure. Three different region representations are proposed using the matrix notation. An additional procedure merges similar regions into larger ones to reduce human expert engagement in region labelling. The algorithm has been evaluated on the number of benchmark datasets to investigate the influence of algorithm parameters on the final performance. Comparison with competing methods proved that the considered algorithm is an interesting proposition in hyperspectral image analysis tasks.},
author = {Ksieniewicz, Pawe{\l} and Jankowski, Dariusz and Ayerdi, Borja and Jackowski, Konrad and Gra{\~{n}}a, Manuel and Wo{\'{z}}niak, Micha{\l}},
file = {:Users/xehivs/Library/Application Support/Mendeley Desktop/Downloaded/Ksieniewicz et al. - 2014 - A novel hyperspectral segmentation algorithm-concept and evaluation.pdf:pdf},
journal = {Logic Journal of the IGPL},
keywords = {Active learning,Hyperspectral image classification,Hyperspectral imaging,Quadtree},
number = {1},
pages = {105--120},
publisher = {Oxford University Press},
title = {{A novel hyperspectral segmentation algorithm-concept and evaluation}},
volume = {23},
year = {2014}
}
@inproceedings{Ksieniewicz2016,
abstract = {{\textcopyright} Springer International Publishing Switzerland 2016. Data obtained by hyperspectral imaging gives us enough information to recreate the human vision, and also to extend it by a new methods to extract features coded in a light spectra. This work proposes a set of functions, based on abstraction of natural photoreceptors. The proposed method was employed as the feature extraction for the classification system based on combined approach and compared with other state-of-art methods on the basis of the selected benchmark images.},
author = {Ksieniewicz, Pawe{\l} and Wo{\'{z}}niak, Micha{\l}},
booktitle = {Advances in Intelligent Systems and Computing},
doi = {10.1007/978-3-319-26227-7_44},
file = {:Users/xehivs/Articles/2015{\_}cores/main.pdf:pdf},
isbn = {9783319262253},
issn = {21945357},
keywords = {Artificial photoreceptors,Ensemble classification,Hyperspectral imaging,Machine learning},
pages = {471--479},
title = {{Artificial photoreceptors for ensemble classification of hyperspectral images}},
volume = {403},
year = {2016}
}
@inproceedings{6909582,
author = {Lapin, M and Schiele, B and Hein, M},
booktitle = {2014 IEEE Conference on Computer Vision and Pattern Recognition},
doi = {10.1109/CVPR.2014.186},
issn = {1063-6919},
keywords = {image classification;image representation;learning},
pages = {1434--1441},
title = {{Scalable Multitask Representation Learning for Scene Classification}},
year = {2014}
}
@inproceedings{levy2014neural,
author = {Levy, Omer and Goldberg, Yoav},
booktitle = {Advances in Neural Information Processing Systems},
pages = {2177--2185},
title = {{Neural word embedding as implicit matrix factorization}},
year = {2014}
}
@article{DBLP:journals/tgrs/0009BP13,
author = {Li, Jun and Bioucas-Dias, Jos{\'{e}} M Jose M and Plaza, Antonio and 0009, Jun Li and Bioucas-Dias, Jos{\'{e}} M Jose M and Plaza, Antonio},
journal = {IEEE T. Geoscience and Remote Sensing},
keywords = {dblp},
number = {2},
pages = {844--856},
title = {{Spectral-Spatial Classification of Hyperspectral Data Using Loopy Belief Propagation and Active Learning.}},
url = {http://dblp.uni-trier.de/db/journals/tgrs/tgrs51.html{\#}0009BP13},
volume = {51},
year = {2013}
}
@article{DBLP:journals/tgrs/LiBP11,
author = {Li, Jun and Bioucas-Dias, Jos{\'{e}} M and Plaza, Antonio},
journal = {IEEE T. Geoscience and Remote Sensing},
number = {10},
pages = {3947--3960},
title = {{Hyperspectral Image Segmentation Using a New Bayesian Approach With Active Learning}},
volume = {49},
year = {2011}
}
@inproceedings{6610939,
author = {Li, Q and Cai, W and Feng, D D},
booktitle = {2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)},
doi = {10.1109/EMBC.2013.6610939},
issn = {1094-687X},
keywords = {biological tissues;computerised tomography;disease},
pages = {6079--6082},
title = {{Lung image patch classification with automatic feature learning}},
year = {2013}
}
@article{journals/vlsisp/LuoC11,
author = {Luo, Bin and Chanussot, Jocelyn},
journal = {Signal Processing Systems},
keywords = {dblp},
number = {3},
pages = {457--468},
title = {{Supervised Hyperspectral Image Classification Based on Spectral Unmixing and Geometrical Features.}},
url = {http://dblp.uni-trier.de/db/journals/vlsisp/vlsisp65.html{\#}LuoC11},
volume = {65},
year = {2011}
}
@inproceedings{ma2014,
abstract = {This paper presents a Tibetan component representation learning method for component-based online handwritten Tibetan character recognition. In conventional methods, we designed features manually for Tibetan components. The hand-crafted features are often incomplete and decrease the component recognition accuracy, which influences component-based character recognition performance. To overcome the deficiency, we use three layer deep belief networks to learn automatically representation features for components. Restricted Boltzmann machine is used to construct each hidden layer. The weight parameters of the networks are optimized by greedy layer-wise learning algorithm. Then we combine representation learning based component classifier into our previous integrated segmentation and recognition framework. Finally we add syllable association module to improve the handwriting input speed. Experimental results on MRG-OHTC database show that the component representation learning method gives the promising performance. The proposed method achieves the component-level and character-level recognition rates of 94.78{\%} and 94.09{\%}.},
author = {Ma, L L and Wu, J},
booktitle = {Frontiers in Handwriting Recognition (ICFHR), 2014 14th International Conference on},
doi = {10.1109/ICFHR.2014.60},
issn = {2167-6445},
keywords = {belief networks;feature extraction;handwritten cha},
pages = {317--322},
title = {{A Tibetan Component Representation Learning Method for Online Handwritten Tibetan Character Recognition}},
year = {2014}
}
@article{mairal2010online,
author = {Mairal, Julien and Bach, Francis and Ponce, Jean and Sapiro, Guillermo},
journal = {The Journal of Machine Learning Research},
pages = {19--60},
publisher = {JMLR. org},
title = {{Online learning for matrix factorization and sparse coding}},
volume = {11},
year = {2010}
}
@article{journals/lgrs/MuraVBCB11,
author = {Mura, Mauro Dalla and Villa, Alberto and Benediktsson, Jon Atli and Chanussot, Jocelyn and Bruzzone, Lorenzo},
journal = {IEEE Geosci. Remote Sensing Lett.},
keywords = {dblp},
number = {3},
pages = {542--546},
title = {{Classification of Hyperspectral Images by Using Extended Morphological Attribute Profiles and Independent Component Analysis.}},
url = {http://dblp.uni-trier.de/db/journals/lgrs/lgrs8.html{\#}MuraVBCB11},
volume = {8},
year = {2011}
}
@inproceedings{7365832,
author = {Owhadi-Kareshk, M and Akbarzadeh-T., M R},
booktitle = {Computer and Knowledge Engineering (ICCKE), 2015 5th International Conference on},
doi = {10.1109/ICCKE.2015.7365832},
keywords = {data structures;learning (artificial intelligence)},
pages = {228--233},
title = {{Representation learning by Denoising Autoencoders for Clustering-based Classification}},
year = {2015}
}
@article{1895RSPTA.186..343P,
author = {Pearson, K},
doi = {10.1098/rsta.1895.0010},
journal = {Philosophical Transactions of the Royal Society of London Series A},
pages = {343--414},
title = {{Contributions to the Mathematical Theory of Evolution. II. Skew Variation in Homogeneous Material}},
volume = {186},
year = {1895}
}
@book{petzold_code:_2000,
address = {Redmond, Wash.},
author = {Petzold, Charles},
isbn = {0735611319 9780735611313 {\{}073560505X{\}} 9780735605053},
publisher = {Microsoft Press},
shorttitle = {Code},
title = {{Code: the hidden language of computer hardware and software}},
year = {2000}
}
@article{journals/tgrs/RajanGC08,
author = {Rajan, Suju and Ghosh, Joydeep and Crawford, Melba M},
journal = {IEEE T. Geoscience and Remote Sensing},
keywords = {dblp},
number = {4},
pages = {1231--1242},
title = {{An Active Learning Approach to Hyperspectral Data Classification.}},
url = {http://dblp.uni-trier.de/db/journals/tgrs/tgrs46.html{\#}RajanGC08},
volume = {46},
year = {2008}
}
@article{OPO:OPO552,
author = {Ruskell, G},
doi = {10.1046/j.1475-1313.2000.00552.x},
issn = {1475-1313},
journal = {Ophthalmic and Physiological Optics},
number = {4},
pages = {349--350},
publisher = {Blackwell Publishers},
title = {{The Human Eye, Structure and Function Clyde W. Oyster; Sinauer Associates, Sunderland, MA, 1999, 766 pages, hardback, ISBN 0-87893-645-9, {\pounds}49.95}},
url = {http://dx.doi.org/10.1046/j.1475-1313.2000.00552.x},
volume = {20},
year = {2000}
}
@article{banana,
author = {Samet, Hanan},
journal = {NATO ASI Series},
title = {{Using quadtrees to represent spatial data}},
volume = {F18},
year = {1985}
}
@book{silverman1986density,
author = {Silverman, Bernard W},
publisher = {CRC press},
title = {{Density estimation for statistics and data analysis}},
volume = {26},
year = {1986}
}
@article{Spracklen2012,
annote = {From Duplicate 1 (Observations of increased tropical rainfall preceded by air passage over forests - Spracklen, D V; Arnold, S R; Taylor, C M)

10.1038/nature11390},
author = {{Spracklen D. V.}, S R Arnold and Taylor., C M and Spracklen, D V and Arnold, S R and Taylor, C M},
issn = {0028-0836},
journal = {Nature},
month = {sep},
number = {7415},
pages = {282--285},
publisher = {Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
title = {{Observations of increased tropical rainfall preceded by air passage over forests}},
url = {http://dx.doi.org/10.1038/nature11390 http://www.nature.com/nature/journal/v489/n7415/abs/nature11390.html{\#}supplementary-information},
volume = {489},
year = {2012}
}
@article{journals/lgrs/SrinivasCMNT13,
author = {Srinivas, Umamahesh and 0001, Yi Chen and Monga, Vishal and Nasrabadi, Nasser M and Tran, Trac D},
journal = {IEEE Geosci. Remote Sensing Lett.},
keywords = {dblp},
number = {3},
pages = {505--509},
title = {{Exploiting Sparsity in Hyperspectral Image Classification via Graphical Models.}},
url = {http://dblp.uni-trier.de/db/journals/lgrs/lgrs10.html{\#}SrinivasCMNT13},
volume = {10},
year = {2013}
}
@article{svaetichin_spectral_1956,
annote = {{\{}PMID:{\}} 13444020},
author = {Svaetichin, G},
issn = {0302-2994},
journal = {Acta physiologica Scandinavica. Supplementum},
keywords = {Color Perception,Retina},
number = {134},
pages = {17--46},
title = {{Spectral response curves from single cones}},
volume = {39},
year = {1956}
}
@patent{tamai_image_1999,
abstract = {An image processing apparatus converts color image data into hue, saturation and luminosity data. When a specific color portion and a range are designated in the hue data, processing for reducing tones is executed based on the designated data. Thereafter, a reverse-conversion into color image data is performed, thereby making it possible to output a high-quality image of an illustration. Further, a histogram of designated hue data is generated, processing for reducing tones is executed based on the histogram, and the data is subsequently reverse-converted into color image data. This makes it possible to output a higher quality image of an illustration.},
annote = {{\{}U.S.{\}} Classification: 358/518; 358/501; 358/520; 358/530 International Classification: {\{}G03F{\}} 308},
author = {Tamai, Shunichi},
number = {5949556},
title = {{Image processing suitable for changing the number of colors in an image}},
url = {http://www.google.pl/patents?id=2i4XAAAAEBAJ},
year = {1999}
}
@inproceedings{conf/igarss/TarabalkaCBAF08,
author = {Tarabalka, Yuliya and Chanussot, Jocelyn and Benediktsson, Jon Atli and Angulo, Jesus and Fauvel, Mathieu},
booktitle = {IGARSS (3)},
keywords = {dblp},
pages = {652--655},
publisher = {IEEE},
title = {{Segmentation and Classification of Hyperspectral Data using Watershed.}},
url = {http://dblp.uni-trier.de/db/conf/igarss/igarss2008-3.html{\#}TarabalkaCBAF08},
year = {2008}
}
@article{tyo_principal-components-based_2003,
abstract = {A new pseudocolor mapping strategy for use with spectral imagery is presented. This strategy is based on a principal components analysis of spectral data, and it capitalizes on the similarities between three-color human vision and high-dimensional hyperspectral datasets. The mapping is closely related to three-dimensional versions of scatter plots that are commonly used in remote sensing to visualize the data cloud. The transformation results in final images where the color assigned to each pixel is solely determined by the position within the data cloud. Materials with similar spectral characteristics are presented in similar hues, and basic classification and clustering decisions can be made by the observer. Final images tend to have large regions of desaturated pixels that make the image more readily interpretable. The data cloud is shown to be conical in nature, and materials with common spectral signatures radiate from the origin of the cone, which is not (in general) at the origin of the spectral data. A supervised method for locating the origin of the cone based on identification of clusters in the data is presented, and the effects of proper origin orientation are illustrated.},
author = {Tyo, J S and Konsolakis, A and Diersen, D I and Olsen, R C},
doi = {10.1109/TGRS.2003.808879},
issn = {0196-2892},
journal = {{\{}IEEE{\}} Transactions on Geoscience and Remote Sensing},
keywords = {Clouds,Data visualization,Displays,Humans,Hyperspectral imaging,Hyperspectral sensors,Pixel,Principal component analysis,Remote sensing,Scattering,data cloud visualization,desaturated pixels,display strategy,hyperspectral imagery,multidimensional imagery display,principal components analysis,pseudocolor mapping strategy,spectral characteristics,spectral data,spectral imagery,{\{}3D{\}} scatter plots},
number = {3},
pages = {708--718},
title = {{Principal-components-based display strategy for spectral imagery}},
volume = {41},
year = {2003}
}
@article{tyo_principal-components-based_2003-1,
abstract = {A new pseudocolor mapping strategy for use with spectral imagery is presented. This strategy is based on a principal components analysis of spectral data, and it capitalizes on the similarities between three-color human vision and high-dimensional hyperspectral datasets. The mapping is closely related to three-dimensional versions of scatter plots that are commonly used in remote sensing to visualize the data cloud. The transformation results in final images where the color assigned to each pixel is solely determined by the position within the data cloud. Materials with similar spectral characteristics are presented in similar hues, and basic classification and clustering decisions can be made by the observer. Final images tend to have large regions of desaturated pixels that make the image more readily interpretable. The data cloud is shown to be conical in nature, and materials with common spectral signatures radiate from the origin of the cone, which is not (in general) at the origin of the spectral data. A supervised method for locating the origin of the cone based on identification of clusters in the data is presented, and the effects of proper origin orientation are illustrated.},
author = {Tyo, J S and Konsolakis, A and Diersen, D I and Olsen, R C},
doi = {10.1109/TGRS.2003.808879},
issn = {0196-2892},
journal = {{\{}IEEE{\}} Transactions on Geoscience and Remote Sensing},
keywords = {Clouds,Data visualization,Displays,Humans,Hyperspectral imaging,Hyperspectral sensors,Pixel,Principal component analysis,Remote sensing,Scattering,data cloud visualization,desaturated pixels,display strategy,hyperspectral imagery,multidimensional imagery display,principal component analysis,principal components analysis,pseudocolor mapping strategy,remote sensing,spectral characteristics,spectral data,spectral imagery,{\{}3D{\}} scatter plots},
number = {3},
pages = {708--718},
title = {{Principal-components-based display strategy for spectral imagery}},
volume = {41},
year = {2003}
}
@article{7401039,
author = {van Tulder, G and de Bruijne, M},
doi = {10.1109/TMI.2016.2526687},
issn = {0278-0062},
journal = {IEEE Transactions on Medical Imaging},
keywords = {Boltzmann machines;biological tissues;channel bank},
number = {5},
pages = {1262--1272},
title = {{Combining Generative and Discriminative Representation Learning for Lung CT Analysis With Convolutional Restricted Boltzmann Machines}},
volume = {35},
year = {2016}
}
@inproceedings{conf/igarss/WangWB09,
author = {Wang, Xin-Lu and Waske, Bjorn and Benediktsson, Jon Atli},
booktitle = {IGARSS (4)},
keywords = {dblp},
pages = {944--947},
publisher = {IEEE},
title = {{Ensemble Methods for Spectral-spatial Classification of Urban Hyperspectral Data.}},
url = {http://dblp.uni-trier.de/db/conf/igarss/igarss2009-4.html{\#}WangWB09},
year = {2009}
}
@book{Wozniak2016,
abstract = {{\textcopyright} Springer International Publishing Switzerland 2016. This work reports the research on active learning approach applied to the data stream classification. The chosen characteristics of the proposed frameworks were evaluated on the basis of the wide range of computer experiments carried out on the three benchmark data streams. Obtained results confirmed the usability of proposed method to the data stream classification with the presence of incremental concept drift.},
author = {Wo{\'{z}}niak, Micha{\l} and Cyganek, Bogus{\l}aw and Kasprzak, Andrzej and Ksieniewicz, Pawe{\l} and Walkowiak, Krzysztof},
booktitle = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
doi = {10.1007/978-3-319-32034-2_16},
isbn = {9783319320335},
issn = {16113349 03029743},
keywords = {Active learning,Data stream classification,Pattern classification},
pages = {186--197},
title = {{Active learning classifier for streaming data}},
volume = {9648},
year = {2016}
}
@article{yang2015,
abstract = {The extreme learning machine (ELM), which was originally proposed for ''generalized'' single-hidden layer feedforward neural networks, provides efficient unified learning solutions for the applications of clustering, regression, and classification. It presents competitive accuracy with superb efficiency in many applications. However, ELM with subnetwork nodes architecture has not attracted much research attentions. Recently, many methods have been proposed for supervised/unsupervised dimension reduction or representation learning, but these methods normally only work for one type of problem. This paper studies the general architecture of multilayer ELM (ML-ELM) with subnetwork nodes, showing that: 1) the proposed method provides a representation learning platform with unsupervised/supervised and compressed/sparse representation learning and 2) experimental results on ten image datasets and 16 classification datasets show that, compared to other conventional feature learning methods, the proposed ML-ELM with subnetwork nodes performs competitively or much better than other feature learning methods.},
author = {Yang, Y and Wu, Q M J},
doi = {10.1109/TCYB.2015.2481713},
issn = {2168-2267},
journal = {IEEE Transactions on Cybernetics},
keywords = {Artificial neural networks;Feature extraction;Hafn},
number = {99},
pages = {1--14},
title = {{Multilayer Extreme Learning Machine With Subnetwork Nodes for Representation Learning}},
volume = {PP},
year = {2015}
}
@article{Yang2015,
author = {Yang, Yimin and Wu, Q M Jonathan and Member, Senior},
doi = {10.1109/TCYB.2015.2481713},
issn = {2168-2267},
pages = {1--14},
title = {{Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification}},
year = {2015}
}
@inproceedings{Zhuang2015a,
author = {Zhuang, Fuzhen and Luo, Dan and Jin, Xin and Xiong, Hui and Luo, Ping and He, Qing},
booktitle = {ICDM},
title = {{Representation Learning via Semi-supervised Autoencoder for Multi-task Learning}},
year = {2015}
}

repository: xehivs/eceReport

repo_name: xehivs/eceReport


text:
// Copyright 2020 thinkgos (thinkgo@aliyun.com).  All rights reserved.
// Use of this source code is governed by a version 3 of the GNU General
// Public License, license that can be found in the LICENSE file.

package iec61850

import (
	"errors"
)

// error defined
var (
	ErrUseClosedConnection = errors.New("use of closed connection")
	ErrBufferFulled        = errors.New("buffer is full")
	ErrNotActive           = errors.New("server is not active")
)

license: MIT

license_type: permissive

branch_name: refs/heads/master

extension: go


text:
// Copyright © 2018 Elliott Davis <elliott@excellent.io>
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.

package pkg

import "github.com/spf13/cobra"

// BinlinkCmd ...
var BinlinkCmd = &cobra.Command{
	Use:   "binlink <PKG_IDENT> [BINARY]",
	Short: "Creates a binlink for a package binary in a common 'PATH' location",
	Args:  cobra.RangeArgs(1, 2),
}

func init() {
	BinlinkCmd.Flags().BoolP("force", "f", false, "Overwrite existing binlinks")
	BinlinkCmd.Flags().StringP("dest", "d", "", "Sets the destination directory (default: /bin)")
}

repository: chuckleheads/biome-go

repo_name: chuckleheads/biome-go


text:
SUMMARY = "Free and Open On-Chip Debugging, In-System Programming and Boundary-Scan Testing"
HOMEPAGE = "http://openocd.org"
LICENSE = "GPL-2.0-only"
LIC_FILES_CHKSUM = "file://COPYING;md5=599d2d1ee7fc84c0467b3d19801db870"

require openocd-stm32mp.inc

SRC_URI = "git://github.com/openocd-org/openocd.git;protocol=https;branch=master;name=openocd "

SRCREV_FORMAT = "openocd"
SRCREV_openocd = "fdf17dba569ac8aca0771c28b661e3722d776541"

PV = "0.11.0+dev.${SRCPV}"

SRC_URI += ""

# Use jimtcl master branch to fix RANLIB issue in kirkstone and commit it
# to prevent "-dirty" suffix to openocd version.
# To be removed after a new jimtcl release get used by openocd.
do_configure:prepend() {
	git add jimtcl
	git commit --allow-empty -m "Update jimtcl"
}

branch_name: refs/heads/mickledore

gha_license_id: NOASSERTION

extension: bb


text:
#
# Base recipe: meta/recipes-graphics/xorg-proto/xcb-proto_1.10.bb
# Base branch: daisy
#

include xcb-proto.inc

PR = "${INC_PR}.0"

LICENSE = "MIT"
LIC_FILES_CHKSUM = " \
file://COPYING;md5=d763b081cb10c223435b01e00dc0aba7 \
file://src/dri2.xml;beginline=2;endline=28;md5=f8763b13ff432e8597e0d610cf598e65 \
"

# apply debian patch by quilt
DEBIAN_PATCH_TYPE = "quilt"

repository: soderstrom-rikard/meta-debian

repo_name: soderstrom-rikard/meta-debian

branch_name: refs/heads/daisy


text:
/*
 * idp_config.js - Application configuration for IDP servers.
 */

MYLAR_ACTIVE_ATTACKER = true;
// use IDP only if active attacker
Accounts.config({sendVerificationEmail:active_attacker(),
                 loginExpirationInDays:1});

MYLAR_IDP_PUB = '8a7fe03431b5fc2db3923a2ab6d1a5ddf35cd64aea35e743' +
                'ded7655f0dc7e085858eeec06e1c7da58c509d57da56dbe6';
MYLAR_IDP_HOSTS = ["http://localhost:3000"]

// Choose a random IDP server to use.
// MYLAR_IDP_HOSTS and MYLAR_IDP_PUB must be defined in an application as
// package-scope variables (variables with no 'var' declaration).
// MYLAR_IDP_HOSTS  - array of strings, hosts of the IDP servers
// MYLAR_IDP_PUB    - string, the common public key of all the IDP servers
MYLAR_CHOSEN_IDP_HOST = Random.choice(MYLAR_IDP_HOSTS);
idp_init(MYLAR_CHOSEN_IDP_HOST, MYLAR_IDP_PUB, false);

repository: sportsbitenews/MylarDistributedDB

repo_name: sportsbitenews/MylarDistributedDB

extension: js


text:
@article{jiang2017deep,
  title={A deep reinforcement learning framework for the financial portfolio management problem},
  author={Jiang, Zhengyao and Xu, Dixing and Liang, Jinjun},
  journal={arXiv preprint arXiv:1706.10059},
  year={2017}
}

@inproceedings{jiang2017cryptocurrency,
  title={Cryptocurrency portfolio management with deep reinforcement learning},
  author={Jiang, Zhengyao and Liang, Jinjun},
  booktitle={2017 Intelligent Systems Conference (IntelliSys)},
  pages={905--913},
  year={2017},
  organization={IEEE}
}

@article{li2014online,
  title={Online portfolio selection: A survey},
  author={Li, Bin and Hoi, Steven CH},
  journal={ACM Computing Surveys (CSUR)},
  volume={46},
  number={3},
  pages={35},
  year={2014},
  publisher={ACM}
}

@article{JMLR:v17:15-317,
  author  = {Bin Li and Doyen Sahoo and Steven C.H. Hoi},
  title   = {OLPS: A Toolbox for On-Line Portfolio Selection},
  journal = {Journal of Machine Learning Research},
  year    = {2016},
  volume  = {17},
  number  = {35},
  pages   = {1-5},
  url     = {http://jmlr.org/papers/v17/15-317.html}
}

@article{silver2017mastering,
  title={Mastering the game of go without human knowledge},
  author={Silver, David and Schrittwieser, Julian and Simonyan, Karen and Antonoglou, Ioannis and Huang, Aja and Guez, Arthur and Hubert, Thomas and Baker, Lucas and Lai, Matthew and Bolton, Adrian and others},
  journal={Nature},
  volume={550},
  number={7676},
  pages={354},
  year={2017},
  publisher={Nature Publishing Group}
}

@article{mnih2015human,
  title={Human-level control through deep reinforcement learning},
  author={Mnih, Volodymyr and Kavukcuoglu, Koray and Silver, David and Rusu, Andrei A and Veness, Joel and Bellemare, Marc G and Graves, Alex and Riedmiller, Martin and Fidjeland, Andreas K and Ostrovski, Georg and others},
  journal={Nature},
  volume={518},
  number={7540},
  pages={529},
  year={2015},
  publisher={Nature Publishing Group}
}

@book{Grinberg:2014:FWD:2621997,
 author = {Grinberg, Miguel},
 title = {Flask Web Development: Developing Web Applications with Python},
 year = {2014},
 isbn = {1449372627, 9781449372620},
 edition = {1st},
 publisher = {O'Reilly Media, Inc.},
}

@inproceedings{bergstra2011algorithms,
  title={Algorithms for hyper-parameter optimization},
  author={Bergstra, James S and Bardenet, R{\'e}mi and Bengio, Yoshua and K{\'e}gl, Bal{\'a}zs},
  booktitle={Advances in neural information processing systems},
  pages={2546--2554},
  year={2011}
}

@article{sharpe1964,
  title={Capital asset prices: A theory of market equilibrium under conditions of risk},
  author={Sharpe, William F},
  journal={The journal of finance},
  volume={19},
  number={3},
  pages={425--442},
  year={1964},
  publisher={Wiley Online Library}
}

@article{sharpe1994,
  title={The sharpe ratio},
  author={Sharpe, William F},
  journal={The journal of portfolio management},
  volume={21},
  number={1},
  pages={49--58},
  year={1994},
  publisher={Institutional Investor Journals}
}

@inproceedings{magdon2003maximum,
  title={The maximum drawdown of the brownian motion},
  author={Magdon-Ismail, Malik and Atiya, Amir and Pratap, Amrit and Abu-Mostafa, Yaser},
  booktitle={Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on},
  pages={243--247},
  year={2003},
  organization={IEEE}
}

@article{blanchard1979MaxDD,
  title={Speculative bubbles, crashes and rational expectations},
  author={Blanchard, Olivier Jean},
  journal={Economics letters},
  volume={3},
  number={4},
  pages={387--389},
  year={1979},
  publisher={Elsevier}
}

@article{markowitz1952portfolio,
  title={Portfolio selection},
  author={Markowitz, Harry},
  journal={The journal of finance},
  volume={7},
  number={1},
  pages={77--91},
  year={1952},
  publisher={Wiley Online Library}
}

@article{kelly1956new,
  title={A New Interpretation of Information Rate},
  author={Kelly jr, JL},
  journal={the bell system technical journal},
  year={1956}
}

@incollection{kelly2011new,
  title={A new interpretation of information rate},
  author={Kelly Jr, John L},
  booktitle={The Kelly Capital Growth Investment Criterion: Theory and Practice},
  pages={25--34},
  year={2011},
  publisher={World Scientific}
}

@article{cover1991universal,
  title={Universal portfolios},
  author={Cover, Thomas M},
  journal={Mathematical finance},
  volume={1},
  number={1},
  pages={1--29},
  year={1991},
  publisher={Wiley Online Library}
}

@article{borodin2004can,
  title={Can We Learn to Beat the Best Stock.},
  author={Borodin, Allan and El-Yaniv, Ran and Gogan, Vincent},
  journal={J. Artif. Intell. Res.(JAIR)},
  volume={21},
  pages={579--594},
  year={2004}
}

@article{li2015moving,
  title={Moving average reversion strategy for on-line portfolio selection},
  author={Li, Bin and Hoi, Steven CH and Sahoo, Doyen and Liu, Zhi-Yong},
  journal={Artificial Intelligence},
  volume={222},
  pages={104--123},
  year={2015},
  publisher={Elsevier}
}

@article{li2013confidence,
  title={Confidence weighted mean reversion strategy for online portfolio selection},
  author={Li, Bin and Hoi, Steven CH and Zhao, Peilin and Gopalkrishnan, Vivekanand},
  journal={ACM Transactions on Knowledge Discovery from Data (TKDD)},
  volume={7},
  number={1},
  pages={4},
  year={2013},
  publisher={ACM}
}

@inproceedings{agarwal2006algorithms,
  title={Algorithms for portfolio management based on the newton method},
  author={Agarwal, Amit and Hazan, Elad and Kale, Satyen and Schapire, Robert E},
  booktitle={Proceedings of the 23rd international conference on Machine learning},
  pages={9--16},
  year={2006},
  organization={ACM}
}

@inproceedings{huang2013robust,
  title={Robust Median Reversion Strategy for On-Line Portfolio Selection.},
  author={Huang, Dingjiang and Zhou, Junlong and Li, Bin and Hoi, Steven CH and Zhou, Shuigeng},
  booktitle={IJCAI},
  pages={2006--2012},
  year={2013}
}

@article{helmbold1998line,
  title={On-Line Portfolio Selection Using Multiplicative Updates},
  author={Helmbold, David P and Schapire, Robert E and Singer, Yoram and Warmuth, Manfred K},
  journal={Mathematical Finance},
  volume={8},
  number={4},
  pages={325--347},
  year={1998},
  publisher={Wiley Online Library}
}

@article{gyorfi2006nonparametric,
  title={NONPARAMETRIC KERNEL-BASED SEQUENTIAL INVESTMENT STRATEGIES},
  author={Gy{\"o}rfi, L{\'a}szl{\'o} and Lugosi, G{\'a}bor and Udina, Frederic},
  journal={Mathematical Finance},
  volume={16},
  number={2},
  pages={337--357},
  year={2006},
  publisher={Wiley Online Library}
}

@article{li2011corn,
  title={Corn: Correlation-driven nonparametric learning approach for portfolio selection},
  author={Li, Bin and Hoi, Steven CH and Gopalkrishnan, Vivekanand},
  journal={ACM Transactions on Intelligent Systems and Technology (TIST)},
  volume={2},
  number={3},
  pages={21},
  year={2011},
  publisher={ACM}
}

@article{singer1997switching,
  title={Switching portfolios},
  author={Singer, Yoram},
  journal={International Journal of Neural Systems},
  volume={8},
  number={04},
  pages={445--455},
  year={1997},
  publisher={World Scientific}
}

@article{li2012pamr,
author = {Li, Bin and Zhao, Peilin and Hoi, Steven C. H. and Gopalkrishnan, Vivekanand},
doi = {10.1007/s10994-012-5281-z},
issn = {08856125},
journal = {Machine Learning},
keywords = {Mean Reversion,Online learning,Passive aggressive learning,Portfolio selection},
mendeley-groups = {surf},
month = {may},
number = {2},
pages = {221--258},
publisher = {Springer US},
title = {{PAMR: Passive aggressive mean reversion strategy for portfolio selection}},
url = {http://link.springer.com/10.1007/s10994-012-5281-z},
volume = {87},
year = {2012}
}

@article{gyorfi2008nonparametric,
  title={Nonparametric nearest neighbor based empirical portfolio selection strategies},
  author={Gy{\"o}rfi, L{\'a}szl{\'o} and Udina, Frederic and Walk, Harro},
  journal={Statistics \& Decisions International mathematical journal for stochastic methods and models},
  volume={26},
  number={2},
  pages={145--157},
  year={2008},
  publisher={Oldenbourg Wissenschaftsverlag GmbH}
}

@inproceedings{das2011meta,
  title={Meta optimization and its application to portfolio selection},
  author={Das, Puja and Banerjee, Arindam},
  booktitle={Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining},
  pages={1163--1171},
  year={2011},
  organization={ACM}
}

@book{sutton2018reinforcement,
  title={Reinforcement learning: An introduction},
  author={Sutton, Richard S and Barto, Andrew G},
  year={2018}
}

@article{lecun1998gradient,
  title={Gradient-based learning applied to document recognition},
  author={LeCun, Yann and Bottou, L{\'e}on and Bengio, Yoshua and Haffner, Patrick and others},
  journal={Proceedings of the IEEE},
  volume={86},
  number={11},
  pages={2278--2324},
  year={1998},
  publisher={Taipei, Taiwan}
}

@article{hochreiter1997long,
  title={Long short-term memory},
  author={Hochreiter, Sepp and Schmidhuber, J{\"u}rgen},
  journal={Neural computation},
  volume={9},
  number={8},
  pages={1735--1780},
  year={1997},
  publisher={MIT Press}
}

@inproceedings{jordan1986attractor,
  title={Attractor dynamics and parallelism in a connectionist sequential machine},
  author={Jordan, Michael},
  booktitle={Proc. of the Eighth Annual Conference of the Cognitive Science Society (Erlbaum, Hillsdale, NJ), 1986},
  year={1986}
}

@ARTICLE{moody2001drl,
	author={J. Moody and M. Saffell},
	journal={IEEE Transactions on Neural Networks},
	title={Learning to trade via direct reinforcement},
	year={2001},
	volume={12},
	number={4},
	pages={875-889},
	keywords={decision theory;investment;learning (artificial intelligence);optimisation;stochastic systems;stock markets;DR;RRL;S&P 500 Stock Index;T-Bills;asset allocations;differential Sharpe ratio;direct reinforcement learning;financial data;forecasting models;intra-daily currency trader;investment decision-making;investment policies;monthly asset allocation system;portfolio optimization;recurrent reinforcement learning;risk-adjusted investment return optimization;stochastic control problem;trading systems;transaction costs;Adaptive algorithm;Asset management;Decision making;Dynamic programming;Investments;Learning;Optimization methods;Portfolios;Predictive models;Stochastic processes},
	doi={10.1109/72.935097},
	ISSN={1045-9227},
	month={Jul},
}

@article{moody1998performance,
  title={Performance functions and reinforcement learning for trading systems and portfolios},
  author={Moody, John and Wu, Lizhong and Liao, Yuansong and Saffell, Matthew},
  journal={Journal of Forecasting},
  volume={17},
  number={56},
  pages={441--470},
  year={1998}
}

@inproceedings{borodin2000competitive,
  title={On the competitive theory and practice of portfolio selection},
  author={Borodin, Allan and El-Yaniv, Ran and Gogan, Vincent},
  booktitle={Latin American symposium on theoretical informatics},
  pages={173--196},
  year={2000},
  organization={Springer}
}

@article{tsang2004eddie,
  title={EDDIE-Automation, a decision support tool for financial forecasting},
  author={Tsang, Edward and Yung, Paul and Li, Jin},
  journal={Decision Support Systems},
  volume={37},
  number={4},
  pages={559--565},
  year={2004},
  publisher={Elsevier}
}

@inproceedings{kimoto1990stock,
  title={Stock market prediction system with modular neural networks},
  author={Kimoto, Takashi and Asakawa, Kazuo and Yoda, Morio and Takeoka, Masakazu},
  booktitle={1990 IJCNN international joint conference on neural networks},
  pages={1--6},
  year={1990},
  organization={IEEE}
}

@article{tay2002modified,
  title={Modified support vector machines in financial time series forecasting},
  author={Tay, Francis EH and Cao, LJ},
  journal={Neurocomputing},
  volume={48},
  number={1-4},
  pages={847--861},
  year={2002},
  publisher={Elsevier}
}

@article{mahfoud1996financial,
  title={Financial forecasting using genetic algorithms},
  author={Mahfoud, Sam and Mani, Ganesh},
  journal={Applied artificial intelligence},
  volume={10},
  number={6},
  pages={543--566},
  year={1996},
  publisher={Taylor \& Francis}
}

@software{anaconda,
  author = {{Anaconda}},
  title = {Anaconda Software Distribution},
  url = {https://anaconda.com},
  version = {2-2.4.0},
  date = {2016-11-01},
}

repository: xjtlu-lug/xjtlu-fyp-design-report-latex

repo_name: xjtlu-lug/xjtlu-fyp-design-report-latex

extension: bib


text:
package main

//go-build: CGO_ENABLED=0

import (
	"flag"

	fx "github.com/robotalks/robo.go/pkg/framework"
	"github.com/robotalks/robo.go/pkg/l1"
	env "github.com/robotalks/robo.go/pkg/l1/env/controller"
	navbot "github.com/robotalks/robo.go/pkg/sim/bots/nav"
	"github.com/robotalks/robo.go/pkg/sim/visualization/see"
)

const (
	imageSVG = `<svg xmlns="http://www.w3.org/2000/svg" viewBox="-150 -150 300 300">
		<g>
			<rect x="-100" y="-100" width="160" height="50" rx="5" />
			<rect x="-100" y="50" width="160" height="50" rx="5" />
			<circle cx="-45" cy="0" r="45" fill="none" stroke="black" stroke-width="2" />
			<path d="M 15 -50 L 100 0 L 15 50 Z" />
		</g>
	</svg>`
)

func init() {
	env.SetControllerType("sim-nav", l1.ControllerMeta{Description: "Simulation: navigation"})
	env.SetupFlags()
	see.SetupFlags()
	navbot.SetupFlags()
}

func main() {
	flag.Parse()

	env := env.NewConfig().MustNewEnv()
	bot := navbot.NewConfig().NewController(env)
	vis := see.NewConfig().NewAdapter()
	vis.Mapper = see.MapObjectFunc(func(obj see.VisibleObject) []see.Object {
		return []see.Object{
			see.ObjectFrom("image", obj).With("src", "data:image/svg+xml;utf8,"+imageSVG),
		}
	})
	vis.Subscribe(bot)

	fx.NewLoop().
		Add(env, bot, vis).
		RunOrFail()
}


text:
SUMMARY = "C++/Boost Asio based websocket client/server library."
SECTION = "libs/network"
HOMEPAGE = "https://github.com/zaphoyd/websocketpp"
LICENSE = "BSD-3-Clause"
LIC_FILES_CHKSUM = "file://${S}/COPYING;md5=4d168d763c111f4ffc62249870e4e0ea"
DEPENDS = "openssl boost zlib"

SRC_URI = "git://github.com/zaphoyd/websocketpp.git;protocol=https \
           file://0001-Replace-make_shared-with-new-in-some-cases.patch \
           file://0002-Fix-missed-entries-fix-testing.patch \
           file://0001-cmake-Use-GNUInstallDirs.patch \
          "

# tag 0.8.1
SRCREV= "c6d7e295bf5a0ab9b5f896720cc1a0e0fdc397a7"

S = "${WORKDIR}/git"

inherit cmake

repository: jetjiang-del/meta-openembedded

repo_name: jetjiang-del/meta-openembedded


text:
@inproceedings{1877461/PZBR4C34,
	title = {Data {Structures} for {Statistical} {Computing} in {Python}},
	booktitle = {Proceedings of the 9th {Python} in {Science} {Conference}},
	author = {McKinney, Wes},
	editor = {Walt, Stéfan van der and Millman, Jarrod},
	year = {2010},
	pages = {51 -- 56}
}

@misc{maturilli_homogenized_2016,
	title = {Homogenized radiosonde record at station {Ny}-Ålesund, {Spitsbergen}, 1993-2014, supplement to: {Maturilli}, {Marion}; {Kayser}, {Markus} (2016): {Arctic} warming, moisture increase and circulation changes observed in the {Ny}-Ålesund homogenized radiosonde record. {Theoretical} and {Applied} {Climatology}, 17 pp},
	shorttitle = {Homogenized radiosonde record at station {Ny}-Ålesund, {Spitsbergen}, 1993-2014, supplement to},
	abstract = {Radiosonde measurements obtained at the Arctic site Ny-Ålesund (78.9°N, 11.9°E), Svalbard, from 1993 to 2014 have been homogenized accounting for instrumentation discontinuities by correcting known errors in the manufacturer provided profiles. From the homogenized data record, the first Ny-Ålesund upper-air climatology of wind, temperature and humidity is presented, forming the background for the analysis of changes during the 22-year period. Particularly during the winter season, a strong increase in atmospheric temperature and humidity is observed, with a significant warming of the free troposphere in January and February up to 3 K per decade. This winter warming is even more pronounced in the boundary layer below 1 km, presumably amplified by mesoscale processes including e.g. orographic effects or the boundary layer capping inversion. Though the largest contribution to the increasing atmospheric water vapour column in winter originates from the lowermost 2 km, no increase in the contribution by specific humidity inversions is detected. Instead, we find an increase in the humidity content of the large-scale background humidity profiles. At the same time, the tropospheric flow in winter is found to occur less frequent from northerly directions and to the same amount more frequent from the South. We conclude that changes in the atmospheric circulation lead to an enhanced advection of warm and moist air from lower latitudes to the Svalbard region in the winter season, causing the warming and moistening of the atmospheric column above Ny-Ålesund, and link the observations to changes in the Arctic Oscillation.},
	language = {eng},
	publisher = {PANGAEA - Data Publisher for Earth \& Environmental Science},
	author = {Maturilli, Marion and Kayser, Markus},
	year = {2016},
	note = {DOI: 10.1594/PANGAEA.845373}
}

@misc{maturilli_homogenized_2017,
	title = {Homogenized radiosonde record at station {Ny}-Ålesund, {Spitsbergen}, 2015-2016},
	language = {eng},
	publisher = {PANGAEA - Data Publisher for Earth \& Environmental Science},
	author = {Maturilli, Marion and Kayser, Markus},
	year = {2017},
	note = {DOI: 10.1594/PANGAEA.875196}
}

@article{rousseeuw_silhouettes:_1987,
	title = {Silhouettes: {A} graphical aid to the interpretation and validation of cluster analysis},
	volume = {20},
	issn = {03770427},
	shorttitle = {Silhouettes},
	url = {http://linkinghub.elsevier.com/retrieve/pii/0377042787901257},
	doi = {10.1016/0377-0427(87)90125-7},
	language = {en},
	urldate = {2017-11-10},
	journal = {Journal of Computational and Applied Mathematics},
	author = {Rousseeuw, Peter J.},
	month = nov,
	year = {1987},
	pages = {53--65}
}

@article{lloyd_least_1982,
	title = {Least squares quantization in {PCM}},
	volume = {28},
	issn = {0018-9448},
	url = {http://ieeexplore.ieee.org/document/1056489/},
	doi = {10.1109/TIT.1982.1056489},
	language = {en},
	number = {2},
	urldate = {2017-11-10},
	journal = {IEEE Transactions on Information Theory},
	author = {Lloyd, S.},
	month = mar,
	year = {1982},
	pages = {129--137}
}

@inproceedings{arthur_k-means++:_2007,
	title = {k-means++: {The} advantages of careful seeding},
	isbn = {0-89871-624-1},
	publisher = {Society for Industrial and Applied Mathematics},
	author = {Arthur, David and Vassilvitskii, Sergei},
	year = {2007},
	pages = {1027--1035}
}

@article{maturilli_arctic_2017,
	title = {Arctic warming, moisture increase and circulation changes observed in the {Ny}-Ålesund homogenized radiosonde record},
	volume = {130},
	issn = {0177-798X, 1434-4483},
	url = {http://link.springer.com/10.1007/s00704-016-1864-0},
	doi = {10.1007/s00704-016-1864-0},
	language = {en},
	number = {1-2},
	urldate = {2017-11-13},
	journal = {Theoretical and Applied Climatology},
	author = {Maturilli, Marion and Kayser, Markus},
	month = oct,
	year = {2017},
	pages = {1--17}
}

@article{maturilli_climatology_2013,
	title = {Climatology and time series of surface meteorology in {Ny}-Ålesund, {Svalbard}},
	volume = {5},
	issn = {1866-3516},
	url = {http://www.earth-syst-sci-data.net/5/155/2013/},
	doi = {10.5194/essd-5-155-2013},
	language = {en},
	number = {1},
	urldate = {2017-11-13},
	journal = {Earth System Science Data},
	author = {Maturilli, M. and Herber, A. and König-Langlo, G.},
	month = apr,
	year = {2013},
	pages = {155--163}
}


text:
DESCRIPTION = "ATA over Ethernet Tools"
SECTION = "console/network"
RRECOMMENDS_${PN} = "kernel-module-aoe"
PR = "r1"
LICENSE = "GPL"

SRC_URI = "${SOURCEFORGE_MIRROR}/${PN}/${PN}-${PV}.tar.gz"

inherit autotools

SRC_URI[md5sum] = "6a77c8a017ef8dc0a5310db944b30035"
SRC_URI[sha256sum] = "caba504ca4f769c5f3f8b4e97ed0da1888bd0be2efd3c33cac9bf36e7c285bc3"

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD