Table of Contents

• Convergent Gradient Ascent in General-Sum GamesBikramjit Banerjee, Jing Peng. 1-9 [doi]
• Revising Engineering Models: Combining Computational Discovery with KnowledgeStephen D. Bay, Daniel G. Shapiro, Pat Langley. 10-22 [doi]
• Variational Extensions to EM and Multinomial PCAWray L. Buntine. 23-34 [doi]
• Learning and Inference for Clause IdentificationXavier Carreras, Lluís Màrquez, Vasin Punyakanok, Dan Roth. 35-47 [doi]
• An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal NetworksHonghua Dai, Gang Li, Yiqing Tu. 48-59 [doi]
• Variance Optimized BaggingPhilip Derbeko, Ran El-Yaniv, Ron Meir. 60-71 [doi]
• How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the CodeGünther Eibl, Karl Peter Pfeiffer. 72-83 [doi]
• Sparse Online Greedy Support Vector RegressionYaakov Engel, Shie Mannor, Ron Meir. 84-96 [doi]
• Pairwise Classification as an Ensemble TechniqueJohannes Fürnkranz. 97-110 [doi]
• RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal NeighbourhoodGrzegorz Góra, Arkadiusz Wojna. 111-123 [doi]
• Using Hard Classifiers to Estimate Conditional Class ProbabilitiesOle Martin Halck. 124-134 [doi]
• Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear LearnerHarlan D. Harris. 135-147 [doi]
• Scaling Boosting by Margin-Based Inclusionof Features and RelationsSusanne Hoche, Stefan Wrobel. 148-160 [doi]
• Multiclass Alternating Decision TreesGeoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank, Mark Hall. 161-172 [doi]
• Possibilistic Induction in Decision-Tree LearningEyke Hüllermeier. 173-184 [doi]
• Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov ChainsChristopher Kermorvant, Pierre Dupont. 185-194 [doi]
• Collaborative Learning of Term-Based Concepts for Automatic Query ExpansionStefan Klink, Armin Hust, Markus Junker, Andreas Dengel. 195-206 [doi]
• Learning to Play a Highly Complex Game from Human Expert GamesTony Kråkenes, Ole Martin Halck. 207-218 [doi]
• Reliable Classifications with Machine LearningMatjaz Kukar, Igor Kononenko. 219-231 [doi]
• Robustness Analyses of Instance-Based Collaborative RecommendationNicholas Kushmerick. 232-244 [doi]
• iBoost: Boosting Using an i nstance-Based Exponential Weighting SchemeStephen Kwek, Chau Nguyen. 245-257 [doi]
• Towards a Simple Clustering Criterion Based on Minimum Length EncodingMarcus-Christopher Ludl, Gerhard Widmer. 258-269 [doi]
• Class Probability Estimation and Cost-Sensitive Classification DecisionsDragos D. Margineantu. 270-281 [doi]
• On-Line Support Vector Machine RegressionMario Martin. 282-294 [doi]
• Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement LearningIshai Menache, Shie Mannor, Nahum Shimkin. 295-306 [doi]
• A Multistrategy Approach to the Classification of Phases in Business CyclesKatharina Morik, Stefan Rüping. 307-318 [doi]
• A Robust Boosting AlgorithmRichard Nock, Patrice Lefaucheur. 319-330 [doi]
• Case Exchange Strategies in Multiagent LearningSantiago Ontañón, Enric Plaza. 331-344 [doi]
• Inductive Confidence Machines for RegressionHarris Papadopoulos, Kostas Proedrou, Volodya Vovk, Alexander Gammerman. 345-356 [doi]
• Macro-Operators in Multirelational Learning: A Search-Space Reduction TechniqueLourdes Peña Castillo, Stefan Wrobel. 357-368 [doi]
• Propagation of Q-values in Tabular TD(lambda)Philippe Preux. 369-380 [doi]
• Transductive Confidence Machines for Pattern RecognitionKostas Proedrou, Ilia Nouretdinov, Volodya Vovk, Alexander Gammerman. 381-390 [doi]
• Characterizing Markov Decision ProcessesBohdana Ratitch, Doina Precup. 391-404 [doi]
• Phase Transitions and Stochastic Local Search in k-Term DNF LearningUlrich Rückert, Stefan Kramer, Luc De Raedt. 405-417 [doi]
• Discriminative Clustering: Optimal Contingency Tables by Learning MetricsJanne Sinkkonen, Samuel Kaski, Janne Nikkilä. 418-430 [doi]
• Boosting Density Function EstimatorsFranck Thollard, Marc Sebban, Philippe Ézéquel. 431-443 [doi]
• Ranking with Predictive Clustering TreesLjupco Todorovski, Hendrik Blockeel, Saso Dzeroski. 444-455 [doi]
• Support Vector Machines for Polycategorical ClassificationIoannis Tsochantaridis, Thomas Hofmann. 456-467 [doi]
• Learning Classification with Both Labeled and Unlabeled DataJean-Noël Vittaut, Massih-Reza Amini, Patrick Gallinari. 468-479 [doi]
• An Information Geometric Perspective on Active LearningChen-Hsiang Yeang. 480-492 [doi]
• Stacking with an Extended Set of Meta-level Attributes and MLRBernard Zenko, Saso Dzeroski. 493-504 [doi]
• Finding Hidden Factors Using Independent Component AnalysisErkki Oja. 505 [doi]
• Reasoning with ClassifiersDan Roth. 506-510 [doi]
• A Kernel Approach for Learning from almost Orthogonal PatternsBernhard Schölkopf, Jason Weston, Eleazar Eskin, Christina S. Leslie, William Stafford Noble. 511-528 [doi]
• Learning with Mixture Models: Concepts and ApplicationsPadhraic Smyth. 529 [doi]