Table of Contents

• Statistical Debugging Using Latent Topic ModelsDavid Andrzejewski, Anne Mulhern, Ben Liblit, Xiaojin Zhu. 6-17 [doi]
• Learning Balls of Strings with Correction QueriesLeonor Becerra-Bonache, Colin de la Higuera, Jean-Christophe Janodet, Frédéric Tantini. 18-29 [doi]
• Neighborhood-Based Local SensitivityPaul N. Bennett. 30-41 [doi]
• Approximating Gaussian Processes with ::::H:::::::2:::-MatricesSteffen Börm, Jochen Garcke. 42-53 [doi]
• Learning Metrics Between Tree Structured Data: Application to Image RecognitionLaurent Boyer 0002, Amaury Habrard, Marc Sebban. 54-66 [doi]
• Shrinkage Estimator for Bayesian Network ParametersJohn Burge, Terran Lane. 67-78 [doi]
• Level Learning Set: A Novel Classifier Based on Active Contour ModelsXiongcai Cai, Arcot Sowmya. 79-90 [doi]
• Learning Partially Observable Markov Models from First Passage TimesJérôme Callut, Pierre Dupont. 91-103 [doi]
• Context Sensitive Paraphrasing with a Global Unsupervised ClassifierMichael Connor, Dan Roth. 104-115 [doi]
• Dual Strategy Active LearningPinar Donmez, Jaime G. Carbonell, Paul N. Bennett. 116-127 [doi]
• Decision Tree Instability and Active LearningKenneth Dwyer, Robert Holte. 128-139 [doi]
• Constraint Selection by Committee: An Ensemble Approach to Identifying Informative Constraints for Semi-supervised ClusteringDerek Greene, Padraig Cunningham. 140-151 [doi]
• The Cost of Learning Directed CutsThomas Gärtner, Gemma C. Garriga. 152-163 [doi]
• Spectral Clustering and Embedding with Hidden Markov ModelsTony Jebara, Yingbo Song, Kapil Thadani. 164-175 [doi]
• Probabilistic Explanation Based LearningAngelika Kimmig, Luc De Raedt, Hannu Toivonen. 176-187 [doi]
• Graph-Based Domain Mapping for Transfer Learning in General GamesGregory Kuhlmann, Peter Stone. 188-200 [doi]
• Learning to Classify Documents with Only a Small Positive Training SetXiaoli Li, Bing Liu, See-Kiong Ng. 201-213 [doi]
• Structure Learning of Probabilistic Relational Models from Incomplete Relational DataXiao-Lin Li, Zhi-Hua Zhou. 214-225 [doi]
• Stability Based Sparse LSI/PCA: Incorporating Feature Selection in LSI and PCADimitrios Mavroeidis, Michalis Vazirgiannis. 226-237 [doi]
• Bayesian Substructure Learning - Approximate Learning of Very Large Network StructuresAndreas Nägele, Mathäus Dejori, Martin Stetter. 238-249 [doi]
• Efficient Continuous-Time Reinforcement Learning with Adaptive State GraphsGerhard Neumann, Michael Pfeiffer, Wolfgang Maass. 250-261 [doi]
• Source Separation with Gaussian Process ModelsSunHo Park, Seungjin Choi. 262-273 [doi]
• Discriminative Sequence Labeling by Z-Score OptimizationElisa Ricci, Tijl De Bie, Nello Cristianini. 274-285 [doi]
• Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New ApproachesMark Schmidt, Glenn Fung, Rómer Rosales. 286-297 [doi]
• Bayesian Inference for Sparse Generalized Linear ModelsMatthias Seeger, Sebastian Gerwinn, Matthias Bethge. 298-309 [doi]
• Classifier Loss Under Metric UncertaintyDavid B. Skalak, Alexandru Niculescu-Mizil, Rich Caruana. 310-322 [doi]
• Additive Groves of Regression TreesDaria Sorokina, Rich Caruana, Mirek Riedewald. 323-334 [doi]
• Efficient Computation of Recursive Principal Component Analysis for Structured InputAlessandro Sperduti. 335-346 [doi]
• Hinge Rank Loss and the Area Under the ROC CurveHarald Steck. 347-358 [doi]
• Clustering Trees with Instance Level ConstraintsJan Struyf, Saso Dzeroski. 359-370 [doi]
• On Pairwise Naive Bayes ClassifiersJan-Nikolas Sulzmann, Johannes Fürnkranz, Eyke Hüllermeier. 371-381 [doi]
• Separating Precision and Mean in Dirichlet-Enhanced High-Order Markov ModelsRikiya Takahashi. 382-393 [doi]
• Safe Q-Learning on Complete History SpacesStephan Timmer, Martin Riedmiller. 394-405 [doi]
• Random ::::k:::: -Labelsets: An Ensemble Method for Multilabel ClassificationGrigorios Tsoumakas, Ioannis P. Vlahavas. 406-417 [doi]
• Seeing the Forest Through the Trees: Learning a Comprehensible Model from an EnsembleAnneleen Van Assche, Hendrik Blockeel. 418-429 [doi]
• Avoiding Boosting Overfitting by Removing Confusing SamplesAlexander Vezhnevets, Olga Barinova. 430-441 [doi]
• Planning and Learning in Environments with Delayed FeedbackThomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman. 442-453 [doi]
• Analyzing Co-training Style AlgorithmsWei Wang, Zhi-Hua Zhou. 454-465 [doi]
• Policy Gradient CriticsDaan Wierstra, Jürgen Schmidhuber. 466-477 [doi]
• An Improved Model Selection Heuristic for AUCShaomin Wu, Peter A. Flach, Cèsar Ferri Ramirez. 478-489 [doi]
• Finding the Right Family: Parent and Child Selection for Averaged One-Dependence EstimatorsFei Zheng, Geoffrey I. Webb. 490-501 [doi]
• Stepwise Induction of Multi-target Model TreesAnnalisa Appice, Saso Dzeroski. 502-509 [doi]
• Comparing Rule Measures for Predictive Association RulesPaulo J. Azevedo, Alípio Mário Jorge. 510-517 [doi]
• User Oriented Hierarchical Information Organization and RetrievalKorinna Bade, Marcel Hermkes, Andreas Nürnberger. 518-526 [doi]
• Learning a Classifier with Very Few Examples: Analogy Based and Knowledge Based Generation of New Examples for Character RecognitionSabri Bayoudh, Harold Mouchère, Laurent Miclet, Éric Anquetil. 527-534 [doi]
• Weighted Kernel Regression for Predicting Changing DependenciesSteven Busuttil, Yuri Kalnishkan. 535-542 [doi]
• Counter-Example Generation-Based One-Class ClassificationAndrás Bánhalmi, András Kocsor, Róbert Busa-Fekete. 543-550 [doi]
• Test-Cost Sensitive Classification Based on Conditioned Loss FunctionsMumin Cebe, Cigdem Gunduz Demir. 551-558 [doi]
• Probabilistic Models for Action-Based Chinese Dependency ParsingXiangyu Duan, Jun Zhao, Bo Xu. 559-566 [doi]
• Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-SearchDaan Fierens, Jan Ramon, Maurice Bruynooghe, Hendrik Blockeel. 567-574 [doi]
• A Simple Lexicographic Ranker and Probability EstimatorPeter A. Flach, Edson Takashi Matsubara. 575-582 [doi]
• On Minimizing the Position Error in Label RankingEyke Hüllermeier, Johannes Fürnkranz. 583-590 [doi]
• On Phase Transitions in Learning Sparse NetworksGoele Hollanders, Geert Jan Bex, Marc Gyssens, Ronald L. Westra, Karl Tuyls. 591-599 [doi]
• Semi-supervised Collaborative Text ClassificationRong Jin, Ming Wu, Rahul Sukthankar. 600-607 [doi]
• Learning from Relevant Tasks OnlySamuel Kaski, Jaakko Peltonen. 608-615 [doi]
• An Unsupervised Learning Algorithm for Rank AggregationAlexandre Klementiev, Dan Roth, Kevin Small. 616-623 [doi]
• Ensembles of Multi-Objective Decision TreesDragi Kocev, Celine Vens, Jan Struyf, Saso Dzeroski. 624-631 [doi]
• Kernel-Based Grouping of Histogram DataTilman Lange, Joachim M. Buhmann. 632-639 [doi]
• Active Class SelectionR. Lomasky, Carla E. Brodley, M. Aernecke, D. Walt, Mark A. Friedl. 640-647 [doi]
• Sequence Labeling with Reinforcement Learning and Ranking AlgorithmsFrancis Maes, Ludovic Denoyer, Patrick Gallinari. 648-657 [doi]
• Efficient Pairwise ClassificationSang-Hyeun Park, Johannes Fürnkranz. 658-665 [doi]
• Scale-Space Based Weak Regressors for BoostingJin Hyeong Park, Chandan K. Reddy. 666-673 [doi]
• ::::K:::: -Means with Large and Noisy Constraint SetsDan Pelleg, Dorit Baras. 674-682 [doi]
• Towards Interactive Active Learning in Multi-view Feature Sets for Information ExtractionKatharina Probst, Rayid Ghani. 683-690 [doi]
• Principal Component Analysis for Large Scale Problems with Lots of Missing ValuesTapani Raiko, Alexander Ilin, Juha Karhunen. 691-698 [doi]
• Transfer Learning in Reinforcement Learning Problems Through Partial Policy RecyclingJan Ramon, Kurt Driessens, Tom Croonenborghs. 699-707 [doi]
• Class Noise Mitigation Through Instance WeightingUmaa Rebbapragada, Carla E. Brodley. 708-715 [doi]
• Optimizing Feature Sets for Structured DataUlrich Rückert, Stefan Kramer. 716-723 [doi]
• Roulette Sampling for Cost-Sensitive LearningVictor S. Sheng, Charles X. Ling. 724-731 [doi]
• Modeling Highway Traffic VolumesTomás Singliar, Milos Hauskrecht. 732-739 [doi]
• Undercomplete Blind Subspace Deconvolution Via Linear PredictionZoltán Szabó, Barnabás Póczos, András Lörincz. 740-747 [doi]
• Learning an Outlier-Robust Kalman FilterJo-Anne Ting, Evangelos Theodorou, Stefan Schaal. 748-756 [doi]
• Imitation Learning Using Graphical ModelsDeepak Verma, Rajesh P. N. Rao. 757-764 [doi]
• Nondeterministic Discretization of Weights Improves Accuracy of Neural NetworksMarcin Wojnarski. 765-772 [doi]
• Semi-definite Manifold AlignmentLiang Xiong, Fei Wang, Changshui Zhang. 773-781 [doi]
• General Solution for Supervised Graph EmbeddingQubo You, Nanning Zheng, Shaoyi Du, Yang Wu. 782-789 [doi]
• Multi-objective Genetic Programming for Multiple Instance LearningAmelia Zafra, Sebastián Ventura. 790-797 [doi]
• Exploiting Term, Predicate, and Feature Taxonomies in Propositionalization and Propositional Rule LearningMonika Záková, Filip Zelezný. 798-805 [doi]