Table of Contents

• Random Matrices in Data AnalysisDimitris Achlioptas. 1-7 [doi]
• Data PrivacyRakesh Agrawal. 8 [doi]
• Breaking Through the Syntax Barrier: Searching with Entities and RelationsSoumen Chakrabarti. 9-16 [doi]
• Real-World Learning with Markov Logic NetworksPedro Domingos. 17 [doi]
• Strength in Diversity: The Advance of Data AnalysisDavid J. Hand. 18-26 [doi]
• Filtered Reinforcement LearningDouglas Aberdeen. 27-38 [doi]
• Applying Support Vector Machines to Imbalanced DatasetsRehan Akbani, Stephen Kwek, Nathalie Japkowicz. 39-50 [doi]
• Sensitivity Analysis of the Result in Binary Decision TreesIsabelle Alvarez. 51-62 [doi]
• A Boosting Approach to Multiple Instance LearningPeter Auer, Ronald Ortner. 63-74 [doi]
• An Experimental Study of Different Approaches to Reinforcement Learning in Common Interest Stochastic GamesAvi Bab, Ronen I. Brafman. 75-86 [doi]
• Learning from Message Pairs for Automatic Email AnsweringSteffen Bickel, Tobias Scheffer. 87-98 [doi]
• Concept Formation in Expressive Description LogicsNicola Fanizzi, Luigi Iannone, Ignazio Palmisano, Giovanni Semeraro. 99-110 [doi]
• Multi-level Boundary Classification for Information ExtractionAidan Finn, Nicholas Kushmerick. 111-122 [doi]
• An Analysis of Stopping and Filtering Criteria for Rule LearningJohannes Fürnkranz, Peter A. Flach. 123-133 [doi]
• Adaptive Online Time Allocation to Search AlgorithmsMatteo Gagliolo, Viktor Zhumatiy, Jürgen Schmidhuber. 134-143 [doi]
• Model Approximation for HEXQ Hierarchical Reinforcement LearningBernhard Hengst. 144-155 [doi]
• Iterative Ensemble Classification for Relational Data: A Case Study of Semantic Web ServicesAndreas Heß, Nicholas Kushmerick. 156-167 [doi]
• Analyzing Multi-agent Reinforcement Learning Using Evolutionary DynamicsPieter Jan t Hoen, Karl Tuyls. 168-179 [doi]
• Experiments in Value Function Approximation with Sparse Support Vector RegressionTobias Jung, Thomas Uthmann. 180-191 [doi]
• Constructive Induction for Classifying Time SeriesMohammed Waleed Kadous, Claude Sammut. 192-204 [doi]
• Fisher Kernels for Logical SequencesKristian Kersting, Thomas Gärtner. 205-216 [doi]
• The Enron Corpus: A New Dataset for Email Classification ResearchBryan Klimt, Yiming Yang. 217-226 [doi]
• Margin Maximizing Discriminant AnalysisAndrás Kocsor, Kornél Kovács, Csaba Szepesvári. 227-238 [doi]
• Multi-objective Classification with Info-Fuzzy NetworksMark Last. 239-249 [doi]
• Improving Progressive Sampling via Meta-learning on Learning CurvesRui Leite, Pavel Brazdil. 250-261 [doi]
• Methods for Rule Conflict ResolutionTony Lindgren. 262-273 [doi]
• An Efficient Method to Estimate Labelled Sample Size for Transductive LDA(QDA/MDA) Based on Bayes RiskHan Liu, Xiaobin Yuan, Qianying Tang, Rafal Kustra. 274-285 [doi]
• Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset SelectionOscar Luaces, Gustavo F. Bayón, José Ramón Quevedo, Jorge Díez, Juan José del Coz, Antonio Bahamonde. 286-297 [doi]
• Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning FrameworkJangmin O, Jae-Won Lee, Jongwoo Lee, Byoung-Tak Zhang. 298-309 [doi]
• Justification-Based Selection of Training Examples for Case Base ReductionSantiago Ontañón, Enric Plaza. 310-321 [doi]
• Using Feature Conjunctions Across Examples for Learning Pairwise ClassifiersSatoshi Oyama, Christopher D. Manning. 322-333 [doi]
• Feature Selection Filters Based on the Permutation TestPredrag Radivojac, Zoran Obradovic, A. Keith Dunker, Slobodan Vucetic. 334-346 [doi]
• Sparse Distributed Memories for On-Line Value-Based Reinforcement LearningBohdana Ratitch, Doina Precup. 347-358 [doi]
• Improving Random ForestsMarko Robnik-Sikonja. 359-370 [doi]
• The Principal Components Analysis of a Graph, and Its Relationships to Spectral ClusteringMarco Saerens, François Fouss, Luh Yen, Pierre Dupont. 371-383 [doi]
• Using String Kernels to Identify Famous Performers from Their Playing StyleCraig Saunders, David R. Hardoon, John Shawe-Taylor, Gerhard Widmer. 384-395 [doi]
• Associative ClusteringJanne Sinkkonen, Janne Nikkilä, Leo Lahti, Samuel Kaski. 396-406 [doi]
• Learning to Fly Simple and RobustDorian Suc, Ivan Bratko, Claude Sammut. 407-418 [doi]
• Bayesian Network Methods for Traffic Flow Forecasting with Incomplete DataShiliang Sun, Changshui Zhang, Guoqiang Yu, Naijiang Lu, Fei Xiao. 419-428 [doi]
• Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision TreesKai Ming Ting. 429-440 [doi]
• Inducing Polynomial Equations for RegressionLjupco Todorovski, Peter Ljubic, Saso Dzeroski. 441-452 [doi]
• Efficient Hyperkernel Learning Using Second-Order Cone ProgrammingIvor W. Tsang, James T. Kwok. 453-464 [doi]
• Effective Voting of Heterogeneous ClassifiersGrigorios Tsoumakas, Ioannis Katakis, Ioannis P. Vlahavas. 465-476 [doi]
• Convergence and Divergence in Standard and Averaging Reinforcement LearningMarco Wiering. 477-488 [doi]
• Document Representation for One-Class SVMXiaoyun Wu, Rohini K. Srihari, Zhaohui Zheng. 489-500 [doi]
• Naive Bayesian Classifiers for RankingHarry Zhang, Jiang Su. 501-512 [doi]
• Conditional Independence TreesHarry Zhang, Jiang Su. 513-524 [doi]
• Exploiting Unlabeled Data in Content-Based Image RetrievalZhi-Hua Zhou, Ke-Jia Chen, Yuan Jiang. 525-536 [doi]
• Population Diversity in Permutation-Based Genetic AlgorithmKenny Qili Zhu, Ziwei Liu. 537-547 [doi]
• Simultaneous Concept Learning of Fuzzy RulesJacobus van Zyl, Ian Cloete. 548-559 [doi]
• SWITCH: A Novel Approach to Ensemble Learning for Heterogeneous DataRong Jin, Huan Liu. 560-562 [doi]
• Estimating Attributed Central Orders: An Empirical ComparisonToshihiro Kamishima, Hideto Kazawa, Shotaro Akaho. 563-565 [doi]
• Batch Reinforcement Learning with State ImportanceLihong Li, Vadim Bulitko, Russell Greiner. 566-568 [doi]
• Explicit Local Models: Towards Optimal Optimization AlgorithmsJan Poland. 569-571 [doi]
• An Intelligent Model for the Signorini Contact Problem in Belt Grinding ProcessesXiang Zhang, Bernd Kuhlenkötter, Klaus Kneupner. 572-574 [doi]
• Cluster-Grouping: From Subgroup Discovery to ClusteringAlbrecht Zimmermann, Luc De Raedt. 575-577 [doi]