Invited Papers

• 
  Dimitris Achlioptas: Random Matrices in Data Analysis. 1-7
• 
  Rakesh Agrawal: Data Privacy. 8
• 
  Soumen Chakrabarti: Breaking Through the Syntax Barrier: Searching with Entities and Relations. 9-16
• 
  Pedro Domingos: Real-World Learning with Markov Logic Networks. 17
• 
  David J. Hand: Strength in Diversity: The Advance of Data Analysis. 18-26

Contributed Papers

• 
  Douglas Aberdeen: Filtered Reinforcement Learning. 27-38
• 
  Rehan Akbani, Stephen Kwek, Nathalie Japkowicz: Applying Support Vector Machines to Imbalanced Datasets. 39-50
• 
  Isabelle Alvarez: Sensitivity Analysis of the Result in Binary Decision Trees. 51-62
• 
  Peter Auer, Ronald Ortner: A Boosting Approach to Multiple Instance Learning. 63-74
• 
  Avi Bab, Ronen I. Brafman: An Experimental Study of Different Approaches to Reinforcement Learning in Common Interest Stochastic Games. 75-86
• 
  Steffen Bickel, Tobias Scheffer: Learning from Message Pairs for Automatic Email Answering. 87-98
• 
  Nicola Fanizzi, Luigi Iannone, Ignazio Palmisano, Giovanni Semeraro: Concept Formation in Expressive Description Logics. 99-110
• 
  Aidan Finn, Nicholas Kushmerick: Multi-level Boundary Classification for Information Extraction. 111-122
• 
  Johannes Fürnkranz, Peter A. Flach: An Analysis of Stopping and Filtering Criteria for Rule Learning. 123-133
• 
  Matteo Gagliolo, Viktor Zhumatiy, Jürgen Schmidhuber: Adaptive Online Time Allocation to Search Algorithms. 134-143
• 
  Bernhard Hengst: Model Approximation for HEXQ Hierarchical Reinforcement Learning. 144-155
• 
  Andreas Heß, Nicholas Kushmerick: Iterative Ensemble Classification for Relational Data: A Case Study of Semantic Web Services. 156-167
• 
  Pieter Jan't Hoen, Karl Tuyls: Analyzing Multi-agent Reinforcement Learning Using Evolutionary Dynamics. 168-179
• 
  Tobias Jung, Thomas Uthmann: Experiments in Value Function Approximation with Sparse Support Vector Regression. 180-191
• 
  Mohammed Waleed Kadous, Claude Sammut: Constructive Induction for Classifying Time Series. 192-204
• 
  Kristian Kersting, Thomas Gärtner: Fisher Kernels for Logical Sequences. 205-216
• 
  Bryan Klimt, Yiming Yang: The Enron Corpus: A New Dataset for Email Classification Research. 217-226
• 
  András Kocsor, Kornél Kovács, Csaba Szepesvári: Margin Maximizing Discriminant Analysis. 227-238
• 
  Mark Last: Multi-objective Classification with Info-Fuzzy Networks. 239-249
• 
  Rui Leite, Pavel Brazdil: Improving Progressive Sampling via Meta-learning on Learning Curves. 250-261
• 
  Tony Lindgren: Methods for Rule Conflict Resolution. 262-273
• 
  Han Liu, Xiaobin Yuan, Qianying Tang, Rafal Kustra: An Efficient Method to Estimate Labelled Sample Size for Transductive LDA(QDA/MDA) Based on Bayes Risk. 274-285
• 
  Oscar Luaces, Gustavo F. Bayón, José Ramón Quevedo, Jorge Díez, Juan José del Coz, Antonio Bahamonde: Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection. 286-297
• 
  Jangmin O, Jae Won Lee, Jongwoo Lee, Byoung-Tak Zhang: Dynamic Asset Allocation Exploiting Predictors in Reinforcement Learning Framework. 298-309
• 
  Santiago Ontañón, Enric Plaza: Justification-Based Selection of Training Examples for Case Base Reduction. 310-321
• 
  Satoshi Oyama, Christopher D. Manning: Using Feature Conjunctions Across Examples for Learning Pairwise Classifiers. 322-333
• 
  Predrag Radivojac, Zoran Obradovic, A. Keith Dunker, Slobodan Vucetic: Feature Selection Filters Based on the Permutation Test. 334-346
• 
  Bohdana Ratitch, Doina Precup: Sparse Distributed Memories for On-Line Value-Based Reinforcement Learning. 347-358
• 
  Marko Robnik-Sikonja: Improving Random Forests. 359-370
• 
  Marco Saerens, François Fouss, Luh Yen, Pierre Dupont: The Principal Components Analysis of a Graph, and Its Relationships to Spectral Clustering. 371-383
• 
  Craig Saunders, David R. Hardoon, John Shawe-Taylor, Gerhard Widmer: Using String Kernels to Identify Famous Performers from Their Playing Style. 384-395
• 
  Janne Sinkkonen, Janne Nikkilä, Leo Lahti, Samuel Kaski: Associative Clustering. 396-406
• 
  Dorian Suc, Ivan Bratko, Claude Sammut: Learning to Fly Simple and Robust. 407-418
• 
  Shiliang Sun, Changshui Zhang, Guoqiang Yu, Naijiang Lu, Fei Xiao: Bayesian Network Methods for Traffic Flow Forecasting with Incomplete Data. 419-428
• 
  Kai Ming Ting: Matching Model Versus Single Model: A Study of the Requirement to Match Class Distribution Using Decision Trees. 429-440
• 
  Ljupco Todorovski, Peter Ljubic, Saso Dzeroski: Inducing Polynomial Equations for Regression. 441-452
• 
  Ivor W. Tsang, James T. Kwok: Efficient Hyperkernel Learning Using Second-Order Cone Programming. 453-464
• 
  Grigorios Tsoumakas, Ioannis Katakis, Ioannis P. Vlahavas: Effective Voting of Heterogeneous Classifiers. 465-476
• 
  Marco Wiering: Convergence and Divergence in Standard and Averaging Reinforcement Learning. 477-488
• 
  Xiaoyun Wu, Rohini K. Srihari, Zhaohui Zheng: Document Representation for One-Class SVM. 489-500
• 
  Harry Zhang, Jiang Su: Naive Bayesian Classifiers for Ranking. 501-512
• 
  Harry Zhang, Jiang Su: Conditional Independence Trees. 513-524
• 
  Zhi-Hua Zhou, Ke-Jia Chen, Yuan Jiang: Exploiting Unlabeled Data in Content-Based Image Retrieval. 525-536
• 
  Kenny Qili Zhu, Ziwei Liu: Population Diversity in Permutation-Based Genetic Algorithm. 537-547
• 
  Jacobus van Zyl, Ian Cloete: Simultaneous Concept Learning of Fuzzy Rules. 548-559

Posters

• 
  Rong Jin, Huan Liu: SWITCH: A Novel Approach to Ensemble Learning for Heterogeneous Data. 560-562
• 
  Toshihiro Kamishima, Hideto Kazawa, Shotaro Akaho: Estimating Attributed Central Orders: An Empirical Comparison. 563-565
• 
  Lihong Li, Vadim Bulitko, Russell Greiner: Batch Reinforcement Learning with State Importance. 566-568
• 
  Jan Poland: Explicit Local Models: Towards "Optimal" Optimization Algorithms. 569-571
• 
  Xiang Zhang, Bernd Kuhlenkötter, Klaus Kneupner: An Intelligent Model for the Signorini Contact Problem in Belt Grinding Processes. 572-574
• 
  Albrecht Zimmermann, Luc De Raedt: Cluster-Grouping: From Subgroup Discovery to Clustering. 575-577

last updated on Sat May 25 04:55:39 2013 by the dblp team
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