text:
760185252},
doi = {10.1162/153244302760185252},
abstract = {In this article we study the generalization abilities of several classifiers of support vector machine (SVM) type using a certain class of kernels that we call universal. It is shown that the soft margin algorithms with universal kernels are consistent for a large class of classification problems including some kind of noisy tasks provided that the regularization parameter is chosen well. In particular we derive a simple sufficient condition for this parameter in the case of Gaussian RBF kernels. On the one hand our considerations are based on an investigation of an approximation property---the so-called universality---of the used kernels that ensures that all continuous functions can be approximated by certain kernel expressions. This approximation property also gives a new insight into the role of kernels in these and other algorithms. On the other hand the results are achieved by a precise study of the underlying optimization problems of the classifiers. Furthermore, we show consistency for the maximal margin classifier as well as for the soft margin SVM's in the presence of large margins. In this case it turns out that also constant regularization parameters ensure consistency for the soft margin SVM's. Finally we prove that even for simple, noise free classification problems SVM's with polynomial kernels can behave arbitrarily badly.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {67–93},
numpages = {27},
keywords = {pattern recognition, PAC model, support vector machines, computational learning theory, kernel methods}
}

@article{10.1162/153244302760185243,
author = {Tong, Simon and Koller, Daphne},
title = {Support Vector Machine Active Learning with Applications to Text Classification},
year = {2002},
issue_date = {3/1/2002},
publisher = {JMLR.org},
volume = {2},
issn = {1532-4435},
url = {https://doi.org/10.1162/153244302760185243},
doi = {10.1162/153244302760185243},
abstract = {Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {45–66},
numpages = {22},
keywords = {support vector machines, active learning, relevance feedback, selective sampling, classification}
}

@article{10.1162/153244302760185234,
author = {Jonyer, Istvan and Cook, Diane J. and Holder, Lawrence B.},
title = {Graph-Based Hierarchical Conceptual Clustering},
year = {2002},
issue_date = {3/1/2002},
publisher = {JMLR.org},
volume = {2},
issn = {1532-4435},
url = {https://doi.org/10.1162/153244302760185234},
doi = {10.1162/153244302760185234},
abstract = {Hierarchical conceptual clustering has proven to be a useful, although under-explored, data mining technique. A graph-based representation of structural information combined with a substructure discovery technique has been shown to be successful in knowledge discovery. The SUBDUE substructure discovery system provides one such combination of approaches. This work presents SUBDUE and the development of its clustering functionalities. Several examples are used to illustrate the validity of the approach both in structured and unstructured domains, as well as to compare SUBDUE to the Cobweb clustering algorithm. We also develop a new metric for comparing structurally-defined clusterings. Results show that SUBDUE successfully discovers hierarchical clusterings in both structured and unstructured data.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {19–43},
numpages = {25},
keywords = {concept formation, clustering, cluster analysis, structural data, graph match}
}

@article{10.1162/153244302760185225,
author = {Mendelson, Shahar},
title = {On the Size of Convex Hulls of Small Sets},
year = {2002},
issue_date = {3/1/2002},
publisher = {JMLR.org},
volume = {2},
issn = {1532-4435},
url = {https://doi.org/10.1162/153244302760185225},
doi = {10.1162/153244302760185225},
abstract = {We investigate two different notions of "size" which appear naturally in Statistical Learning Theory. We present quantitative estimates on the fat-shattering dimension and on the covering numbers of convex hulls of sets of functions, given the necessary data on the original sets. The proofs we present are relatively simple since they do not require extensive background in convex geometry.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1–18},
numpages = {18},
keywords = {convex hulls, covering numbers, fat-shattering dimension}
}

@article{10.1162/153244302320884605,
author = {F\"{u}rnkranz, Johannes},
title = {Round Robin Classification},
year = {2002},
issue_date = {3/1/2002},
publisher = {JMLR.org},
volume = {2},
issn = {1532-4435},
url = {https://doi.org/10.1162/153244302320884605},
doi = {10.1162/153244302320884605},
abstract = {In this paper, we discuss round robin classification (aka pairwise classification), a technique for handling multi-class problems with binary classifiers by learning one classifier for each pair of classes. We present an empirical evaluation of the method, implemented as a wrapper around the Ripper rule learning algorithm, on 20 multi-class datasets from the UCI database repository. Our results show that the technique is very likely to improve Ripper's classification accuracy without having a high risk of decreasing it. More importantly, we give a general theoretical analysis of the complexity of the approach and show that its run-time complexity is below that of the commonly used one-against-all technique. These theoretical results are not restricted to rule learning but are also of interest to other communities where pairwise classification has recently received some attention. Furthermore, we investigate its properties as a general ensemble technique and show that round robin classification with C5.0 may improve C5.0's performance on multi-class problems. However, this improvement does not reach the performance increase of boosting, and a combination of boosting and round robin classification does not produce any gain over conventional boosting. Finally, we show that the performance of round robin classification can be further improved by a straight-forward integration with bagging.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {721–747},
numpages = {27},
keywords = {class binarization, pairwise classification, ensemble techniques, multi-class problems, inductive rule learning}
}

@article{10.5555/944919.944981,
author = {Torkkola, Kari},
title = {Feature Extraction by Non Parametric Mutual Information Maximization},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We present a method for learning discriminative feature transforms using as criterion the mutual information between class labels and transformed features. Instead of a commonly used mutual information measure based on Kullback-Leibler divergence, we use a quadratic divergence measure, which allows us to make an efficient non-parametric implementation and requires no prior assumptions about class densities. In addition to linear transforms, we also discuss nonlinear transforms that are implemented as radial basis function networks. Extensions to reduce the computational complexity are also presented, and a comparison to greedy feature selection is made.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1415–1438},
numpages = {24}
}

@article{10.5555/944919.944980,
author = {Stoppiglia, Herv\'{e} and Dreyfus, G\'{e}rard and Dubois, R\'{e}mi and Oussar, Yacine},
title = {Ranking a Random Feature for Variable and Feature Selection},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We describe a feature selection method that can be applied directly to models that are linear with respect to their parameters, and indirectly to others. It is independent of the target machine. It is closely related to classical statistical hypothesis tests, but it is more intuitive, hence more suitable for use by engineers who are not statistics experts. Furthermore, some assumptions of classical tests are relaxed. The method has been used successfully in a number of applications that are briefly described.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1399–1414},
numpages = {16}
}

@article{10.5555/944919.944979,
author = {Rivals, Isabelle and Personnaz, L\'{e}on},
title = {Mlps (Mono Layer Polynomials and Multi Layer Perceptrons) for Nonlinear Modeling},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {This paper presents a model selection procedure which stresses the importance of the classic polynomial models as tools for evaluating the complexity of a given modeling problem, and for removing non-significant input variables. If the complexity of the problem makes a neural network necessary, the selection among neural candidates can be performed in two phases. In an additive phase, the most important one, candidate neural networks with an increasing number of hidden neurons are trained. The addition of hidden neurons is stopped when the effect of the round-off errors becomes significant, so that, for instance, confidence intervals cannot be accurately estimated. This phase leads to a set of approved candidate networks. In a subsequent subtractive phase, a selection among approved networks is performed using statistical Fisher tests. The series of tests starts from a possibly too large unbiased network (the full network), and ends with the smallest unbiased network whose input variables and hidden neurons all have a significant contribution to the regression estimate. This method was successfully tested against the real-world regression problems proposed at the NIPS2000 Unlabeled Data Supervised Learning Competition; two of them are included here as illustrative examples.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1383–1398},
numpages = {16}
}

@article{10.5555/944919.944978,
author = {Reunanen, Juha},
title = {Overfitting in Making Comparisons between Variable Selection Methods},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {This paper addresses a common methodological flaw in the comparison of variable selection methods. A practical approach to guide the search or the selection process is to compute cross-validation performance estimates of the different variable subsets. Used with computationally intensive search algorithms, these estimates may overfit and yield biased predictions. Therefore, they cannot be used reliably to compare two selection methods, as is shown by the empirical results of this paper. Instead, like in other instances of the model selection problem, independent test sets should be used for determining the final performance. The claims made in the literature about the superiority of more exhaustive search algorithms over simpler ones are also revisited, and some of them infirmed.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1371–1382},
numpages = {12}
}

@article{10.5555/944919.944977,
author = {Rakotomamonjy, Alain},
title = {Variable Selection Using Svm Based Criteria},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We propose new methods to evaluate variable subset relevance with a view to variable selection. Relevance criteria are derived from Support Vector Machines and are based on weight vector ||w||2 or generalization error bounds sensitivity with respect to a variable. Experiments on linear and non-linear toy problems and real-world datasets have been carried out to assess the effectiveness of these criteria. Results show that the criterion based on weight vector derivative achieves good results and performs consistently well over the datasets we used.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1357–1370},
numpages = {14}
}

@article{10.5555/944919.944976,
author = {Perkins, Simon and Lacker, Kevin and Theiler, James},
title = {Grafting: Fast, Incremental Feature Selection by Gradient Descent in Function Space},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We present a novel and flexible approach to the problem of feature selection, called  grafting . Rather than considering feature selection as separate from learning, grafting treats the selection of suitable features as an integral part of learning a predictor in a regularized learning framework. To make this regularized learning process sufficiently fast for large scale problems, grafting operates in an incremental iterative fashion, gradually building up a feature set while training a predictor model using gradient descent. At each iteration, a fast gradient-based heuristic is used to quickly assess which feature is most likely to improve the existing model, that feature is then added to the model, and the model is incrementally optimized using gradient descent. The algorithm scales linearly with the number of data points and at most quadratically with the number of features. Grafting can be used with a variety of predictor model classes, both linear and non-linear, and can be used for both classification and regression. Experiments are reported here on a variant of grafting for classification, using both linear and non-linear models, and using a logistic regression-inspired loss function. Results on a variety of synthetic and real world data sets are presented. Finally the relationship between grafting, stagewise additive modelling, and boosting is explored.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1333–1356},
numpages = {24}
}

@article{10.5555/944919.944975,
author = {Globerson, Amir and Tishby, Naftali},
title = {Sufficient Dimensionality Reduction},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {Dimensionality reduction of empirical co-occurrence data is a fundamental problem in unsupervised learning. It is also a well studied problem in statistics known as the analysis of cross-classified data. One principled approach to this problem is to represent the data in low dimension with minimal loss of (mutual) information contained in the original data. In this paper we introduce an information theoretic nonlinear method for finding such a most informative dimension reduction. In contrast with previously introduced clustering based approaches, here we extract  continuous feature functions  directly from the co-occurrence matrix. In a sense, we automatically extract functions of the variables that serve as approximate sufficient statistics for a sample of one variable about the other one. Our method is different from dimensionality reduction methods which are based on a specific, sometimes arbitrary, metric or embedding. Another interpretation of our method is as generalized - multi-dimensional - non-linear regression, where rather than fitting one regression function through two dimensional data, we extract  d -regression functions whose expectation values capture the information among the variables. It thus presents a new learning paradigm that unifies aspects from both supervised and unsupervised learning. The resulting dimension reduction can be described by two conjugate d-dimensional differential manifolds that are coupled through Maximum Entropy  I -projections. The Riemannian metrics of these manifolds are determined by the observed expectation values of our extracted features. Following this geometric interpretation we present an iterative information projection algorithm for finding such features and prove its convergence. Our algorithm is similar to the method of "association analysis" in statistics, though the feature extraction context as well as the information theoretic and geometric interpretation are new. The algorithm is illustrated by various synthetic co-occurrence data. It is then demonstrated for text categorization and information retrieval and proves effective in selecting a small set of features, often improving performance over the original feature set.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1307–1331},
numpages = {25}
}

@article{10.5555/944919.944974,
author = {Forman, George},
title = {An Extensive Empirical Study of Feature Selection Metrics for Text Classification},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {Machine learning for text classification is the cornerstone of document categorization, news filtering, document routing, and personalization. In text domains, effective feature selection is essential to make the learning task efficient and more accurate. This paper presents an empirical comparison of twelve feature selection methods (e.g. Information Gain) evaluated on a benchmark of 229 text classification problem instances that were gathered from Reuters, TREC, OHSUMED, etc. The results are analyzed from multiple goal perspectives-accuracy, F-measure, precision, and recall-since each is appropriate in different situations. The results reveal that a new feature selection metric we call 'Bi-Normal Separation' (BNS), outperformed the others by a substantial margin in most situations. This margin widened in tasks with high class skew, which is rampant in text classification problems and is particularly challenging for induction algorithms. A new evaluation methodology is offered that focuses on the needs of the data mining practitioner faced with a single dataset who seeks to choose one (or a pair of) metrics that are most likely to yield the best performance. From this perspective, BNS was the top single choice for all goals except precision, for which Information Gain yielded the best result most often. This analysis also revealed, for example, that Information Gain and Chi-Squared have correlated failures, and so they work poorly together. When choosing optimal pairs of metrics for each of the four performance goals, BNS is consistently a member of the pair---e.g., for greatest recall, the pair BNS + F1-measure yielded the best performance on the greatest number of tasks by a considerable margin.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1289–1305},
numpages = {17}
}

@article{10.5555/944919.944973,
author = {Dhillon, Inderjit S. and Mallela, Subramanyam and Kumar, Rahul},
title = {A Divisive Information Theoretic Feature Clustering Algorithm for Text Classification},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {High dimensionality of text can be a deterrent in applying complex learners such as Support Vector Machines to the task of text classification. Feature clustering is a powerful alternative to feature selection for reducing the dimensionality of text data. In this paper we propose a new information-theoretic divisive algorithm for feature/word clustering and apply it to text classification. Existing techniques for such "distributional clustering" of words are agglomerative in nature and result in (i) sub-optimal word clusters and (ii) high computational cost. In order to explicitly capture the optimality of word clusters in an information theoretic framework, we first derive a global criterion for feature clustering. We then present a fast, divisive algorithm that monotonically decreases this objective function value. We show that our algorithm minimizes the "within-cluster Jensen-Shannon divergence" while simultaneously maximizing the "between-cluster Jensen-Shannon divergence". In comparison to the previously proposed agglomerative strategies our divisive algorithm is much faster and achieves comparable or higher classification accuracies. We further show that feature clustering is an effective technique for building smaller class models in hierarchical classification. We present detailed experimental results using Naive Bayes and Support Vector Machines on the 20Newsgroups data set and a 3-level hierarchy of HTML documents collected from the Open Directory project (www.dmoz.org).},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1265–1287},
numpages = {23}
}

@article{10.5555/944919.944972,
author = {Caruana, Rich and de Sa, Virginia R.},
title = {Benefitting from the Variables That Variable Selection Discards},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {In supervised learning variable selection is used to find a subset of the available inputs that accurately predict the output. This paper shows that some of the variables that variable selection discards can beneficially be used as extra outputs for inductive transfer. Using discarded input variables as extra outputs forces the model to learn mappings from the variables that were selected as inputs to these extra outputs. Inductive transfer makes what is learned by these mappings available to the model that is being trained on the main output, often resulting in improved performance on that main output. We present three synthetic problems (two regression problems and one classification problem) where performance improves if some variables discarded by variable selection are used as extra outputs. We then apply variable selection to two real problems (DNA splice-junction and pneumonia risk prediction) and demonstrate the same effect: using some of the discarded input variables as extra outputs yields somewhat better performance on both of these problems than can be achieved by variable selection alone. This new approach enhances the benefit of variable selection by allowing the learner to benefit from variables that would otherwise have been discarded by variable selection, but without suffering the loss in performance that occurs when these variables are used as inputs.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1245–1264},
numpages = {20}
}

@article{10.5555/944919.944971,
author = {Bi, Jinbo and Bennett, Kristin and Embrechts, Mark and Breneman, Curt and Song, Minghu},
title = {Dimensionality Reduction via Sparse Support Vector Machines},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We describe a methodology for performing variable ranking and selection using support vector machines (SVMs). The method constructs a series of sparse linear SVMs to generate linear models that can generalize well, and uses a subset of nonzero weighted variables found by the linear models to produce a final nonlinear model. The method exploits the fact that a linear SVM (no kernels) with l1-norm regularization inherently performs variable selection as a side-effect of minimizing capacity of the SVM model. The distribution of the linear model weights provides a mechanism for ranking and interpreting the effects of variables. Starplots are used to visualize the magnitude and variance of the weights for each variable. We illustrate the effectiveness of the methodology on synthetic data, benchmark problems, and challenging regression problems in drug design. This method can dramatically reduce the number of variables and outperforms SVMs trained using all attributes and using the attributes selected according to correlation coefficients. The visualization of the resulting models is useful for understanding the role of underlying variables.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1229–1243},
numpages = {15}
}

@article{10.5555/944919.944970,
author = {Bengio, Yoshua and Chapados, Nicolas},
title = {Extensions to Metric Based Model Selection},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {Metric-based methods have recently been introduced for model selection and regularization, often yielding very significant improvements over the alternatives tried (including cross-validation). All these methods require unlabeled data over which to compare functions and detect gross differences in behavior away from the training points. We introduce three new extensions of the metric model selection methods and apply them to feature selection. The first extension takes advantage of the particular case of time-series data in which the task involves prediction with a horizon h. The idea is to use at t the h unlabeled examples that precede t for model selection. The second extension takes advantage of the different error distributions of cross-validation and the metric methods: cross-validation tends to have a larger variance and is unbiased. A hybrid combining the two model selection methods is rarely beaten by any of the two methods. The third extension deals with the case when unlabeled data is not available at all, using an estimated input density. Experiments are described to study these extensions in the context of capacity control and feature subset selection.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1209–1227},
numpages = {19}
}

@article{10.5555/944919.944969,
author = {Bekkerman, Ron and El-Yaniv, Ran and Tishby, Naftali and Winter, Yoad},
title = {Distributional Word Clusters vs. Words for Text Categorization},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We study an approach to text categorization that combines distributional clustering of words and a Support Vector Machine (SVM) classifier. This word-cluster representation is computed using the recently introduced Information Bottleneck method, which generates a compact and efficient representation of documents. When combined with the classification power of the SVM, this method yields high performance in text categorization. This novel combination of SVM with word-cluster representation is compared with SVM-based categorization using the simpler bag-of-words (BOW) representation. The comparison is performed over three known datasets. On one of these datasets (the 20 Newsgroups) the method based on word clusters significantly outperforms the word-based representation in terms of categorization accuracy or representation efficiency. On the two other sets (Reuters-21578 and WebKB) the word-based representation slightly outperforms the word-cluster representation. We investigate the potential reasons for this behavior and relate it to structural differences between the datasets.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1183–1208},
numpages = {26}
}

@article{10.5555/944919.944968,
author = {Guyon, Isabelle and Elisseeff, Andr\'{e}},
title = {An Introduction to Variable and Feature Selection},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {Variable and feature selection have become the focus of much research in areas of application for which datasets with tens or hundreds of thousands of variables are available. These areas include text processing of internet documents, gene expression array analysis, and combinatorial chemistry. The objective of variable selection is three-fold: improving the prediction performance of the predictors, providing faster and more cost-effective predictors, and providing a better understanding of the underlying process that generated the data. The contributions of this special issue cover a wide range of aspects of such problems: providing a better definition of the objective function, feature construction, feature ranking, multivariate feature selection, efficient search methods, and feature validity assessment methods.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1157–1182},
numpages = {26}
}

@article{10.5555/944919.944966,
author = {Bengio, Yoshua and Ducharme, R\'{e}jean and Vincent, Pascal and Janvin, Christian},
title = {A Neural Probabilistic Language Model},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {A goal of statistical language modeling is to learn the joint probability function of sequences of words in a language. This is intrinsically difficult because of the curse of dimensionality: a word sequence on which the model will be tested is likely to be different from all the word sequences seen during training. Traditional but very successful approaches based on n-grams obtain generalization by concatenating very short overlapping sequences seen in the training set. We propose to fight the curse of dimensionality by learning a distributed representation for words which allows each training sentence to inform the model about an exponential number of semantically neighboring sentences. The model learns simultaneously (1) a distributed representation for each word along with (2) the probability function for word sequences, expressed in terms of these representations. Generalization is obtained because a sequence of words that has never been seen before gets high probability if it is made of words that are similar (in the sense of having a nearby representation) to words forming an already seen sentence. Training such large models (with millions of parameters) within a reasonable time is itself a significant challenge. We report on experiments using neural networks for the probability function, showing on two text corpora that the proposed approach significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to take advantage of longer contexts.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1137–1155},
numpages = {19}
}

@article{10.5555/944919.944965,
author = {Barnard, Kobus and Duygulu, Pinar and Forsyth, David and de Freitas, Nando and Blei, David M. and Jordan, Michael I.},
title = {Matching Words and Pictures},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We present a new approach for modeling multi-modal data sets, focusing on the specific case of segmented images with associated text. Learning the joint distribution of image regions and words has many applications. We consider in detail predicting words associated with whole images (auto-annotation) and corresponding to particular image regions (region naming). Auto-annotation might help organize and access large collections of images. Region naming is a model of object recognition as a process of translating image regions to words, much as one might translate from one language to another. Learning the relationships between image regions and semantic correlates (words) is an interesting example of multi-modal data mining, particularly because it is typically hard to apply data mining techniques to collections of images. We develop a number of models for the joint distribution of image regions and words, including several which explicitly learn the correspondence between regions and words. We study multi-modal and correspondence extensions to Hofmann's hierarchical clustering/aspect model, a translation model adapted from statistical machine translation (Brown et al.), and a multi-modal extension to mixture of latent Dirichlet allocation (MoM-LDA). All models are assessed using a large collection of annotated images of real scenes. We study in depth the difficult problem of measuring performance. For the annotation task, we look at prediction performance on held out data. We present three alternative measures, oriented toward different types of task. Measuring the performance of correspondence methods is harder, because one must determine whether a word has been placed on the right region of an image. We can use annotation performance as a proxy measure, but accurate measurement requires hand labeled data, and thus must occur on a smaller scale. We show results using both an annotation proxy, and manually labeled data.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1107–1135},
numpages = {29}
}

@article{10.5555/944919.944964,
author = {Zelenko, Dmitry and Aone, Chinatsu and Richardella, Anthony},
title = {Kernel Methods for Relation Extraction},
year = {2003},
issue_date = {3/1/2003},
publisher = {JMLR.org},
volume = {3},
number = {null},
issn = {1532-4435},
abstract = {We present an application of kernel methods to extracting relations from unstructured natural language sources. We introduce kernels defined over shallow parse representations of text, and design efficient algorithms for computing the kernels. We use the devised kernels in conjunction with Support Vector Machine and Voted Perceptron learning algorithms for the task of extracting <tt>person-affiliation</tt> and <tt>organization-location</tt> relations from text. We experimentally evaluate the proposed methods and compare them with feature-based learning algorithms, with promising results.},
journal = {J. Mach. Learn. Res.},
month = mar,
pages = {1083–1106},
numpages = {24}
}

license: Unlicense

repository: goyalkaraniit/acm-cr

repo_name: goyalkaraniit/acm-cr


text:
@InProceedings{Cortes15,
  author = {Cortes, Corinna and Kuznetsov, Vitaly and Mohri, Mehryar and Warmuth, Manfred},
  title = {On-Line Learning Algorithms for Path Experts with Non-Additive Losses},
  pages = {424-447},
  abstract = {We consider two broad families of non-additive loss functions covering a large number of applications: rational losses and tropical losses. We give new algorithms extending the Follow-the-Perturbed-Leader (FPL) algorithm to both of these families of loss functions and similarly give new algorithms extending the Randomized Weighted Majority (RWM) algorithm to both of these families. We prove that the time complexity of our extensions to rational losses of both FPL and RWM is polynomial and present regret bounds for both.  We further show that these algorithms can play a critical role in improving performance in applications such as structured prediction.
},
}

license: MIT

repository: mreid/papersite

license_type: permissive

repo_name: mreid/papersite

branch_name: refs/heads/master

gha_license_id: MIT

gha_language: TeX

extension: bib


text:
SRC_URI[md5sum] = "dbf516d18e176bbb131de3efccfee533"
SRC_URI[sha256sum] = "c7f1bf99ed8aeba65a485489869e2a50f022f3cd847df85e192fc99fdd6c8b5e"
require libgit2-release.inc

repository: posborne/meta-rust

repo_name: posborne/meta-rust

extension: bb


text:
@article{Zhao2020,
 abstract = {Intranasal oxytocin (OXT) has been associated with effects on diverse social-emotional domains in humans, however progress towards a therapeutic application of OXT in disorders with social-emotion impairments is currently hampered by poor replicability. Limited statistical power and individual differences in biological factors, such as oxytocin receptor (OXTR) genetics, may have contributed to these variable findings. To this end, employing a validated oxytocin-sensitive trait judgment paradigm, we present a pharmaco-genetic study aiming at (1) replicating previous findings suggesting that intranasal oxytocin (24 IU) reduces the self-referential bias in a large sample of n = 170 male subjects, (2) determining whether variations in common receptor polymorphisms (rs237887, rs2268491, rs2254298, rs53576, rs2268498) influence sensitivity to oxytocin's behavioral effects. We confirmed that in the whole sample oxytocin influenced self-other distinction in terms of reduced decision time. However, oxytocin only influenced decision time in rs53576 G carriers, whereas effects on subsequent memory performance were only found in rs2268498 TT homozygotes. In summary, the current study partially replicates our previous findings showing that oxytocin reduces the self-referential bias and suggests that sensitivity to its effects in this domain are receptor genotype dependent.},
 author = {Zhao, Weihua and Luo, Ruixue and Sindermann, Cornelia and Li, Jialin and Wei, Zhenyu and Zhang, Yingying and Liu, Congcong and Le, Jiao and Quintana, Daniel S. and Montag, Christian and Becker, Benjamin and Kendrick, Keith M.},
 copyright = {All rights reserved},
 doi = {10.1016/j.pnpbp.2019.109734},
 issn = {0278-5846},
 journal = {Progress in Neuro-Psychopharmacology and Biological Psychiatry},
 keywords = {Genotype, Oxytocin, Oxytocin receptor, Self-other distinction, Trait judgment},
 month = {January},
 pages = {109734},
 title = {Oxytocin modulation of self-referential processing is partly replicable and sensitive to oxytocin receptor genotype},
 url = {http://www.sciencedirect.com/science/article/pii/S0278584619302891},
 urldate = {2019-08-19},
 volume = {96},
 year = {2020}
}


text:
DESCRIPTION = "LibNSFB is a framebuffer abstraction library, written in C"
HOMEPAGE = "http://www.netsurf-browser.org/projects/libnsfb/"
SECTION = "libs"
PRIORITY = "optional"
LICENSE = "MIT"
DEPENDS = "xcb-util"

SRCREV = "10944"
PV = "0.0.1+svnr${SRCPV}"
PR = "r1"

SRC_URI = "svn://svn.netsurf-browser.org/trunk;module=libnsfb \
	   file://no-werror.patch"

S = ${WORKDIR}/libnsfb

inherit pkgconfig

EXTRA_OEMAKE = "CURDIR=${S} DESTDIR=${D} PREFIX=${prefix} BUILDDIR=build-OE"

# NOTE: we're using default buildmode here, which results in building only
# static libraries (.a) Not a problem as libnsbmp is only used by Netsurf
# at the moment

do_stage() {
	oe_libinstall -a -C build-OE/ libnsfb ${STAGING_LIBDIR}
	install -m 0644 build-OE/libnsfb.pc ${STAGING_LIBDIR}/pkgconfig
	install -m 0644 include/*.h ${STAGING_INCDIR}/
}

do_install() {
	oe_runmake install
}

repository: overo/overo-oe-natty

repo_name: overo/overo-oe-natty

branch_name: HEAD