Front matter
7-16
Convergent Gradient Ascent in General-Sum Games
Bikramjit Banerjee and Jing Peng
1-7
Revising Engineering Models: Combining Computational Discovery with Knowledge
Stephen D. Bay, Daniel G. Shapiro and Pat Langley
23-34
Variational Extensions to EM and Multinomial PCA
Wray Buntine
59-68
Learning and Inference for Clause Identification
Xavier Carreras, Lluís Màrquez, Vasin Punyakanok and Dan Roth
195-209
An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks
Honghua Dai, Gang Li and Yiqing Tu
60-72
Variance Optimized Bagging
Philip Derbeko, Ran El-Yaniv and Ron Meir
169-184
How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code
Günther Eibl and Karl Peter Pfeiffer
1-3
Sparse Online Greedy Support Vector Regression
Yaakov Engel, Shie Mannor and Ron Meir
9-38
Pairwise Classification as an Ensemble Technique
Johannes Fürnkranz
79-144
RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood
Grzegorz Góra and Arkadiusz Wojna
124-134
Using Hard Classifiers to Estimate Conditional Class Probabilities
Ole Martin Halck
23-41
Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner
Harlan D. Harris
1-15
Scaling Boosting by Margin-Based Inclusion of Features and Relations
Susanne Hoche and Stefan Wrobel
105-122
Multiclass Alternating Decision Trees
Geoffrey Holmes, Bernhard Pfahringer, Richard Kirkby, Eibe Frank and Mark Hall
65-79
Possibilistic Induction in Decision-Tree Learning
Eyke Hüllermeier
135-145
Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains
Christopher Kermorvant and Pierre Dupont
195-207
Collaborative Learning of Term-Based Concepts for Automatic Query Expansion
Stefan Klink, Armin Hust, Markus Junker and Andreas Dengel
207-218
Learning to Play a Highly Complex Game from Human Expert Games
Tony Kr°akenes and Ole Martin Halck
1-8
Reliable Classifications with Machine Learning
Matjaž Kukar and Igor Kononenko
232-244
Robustness Analyses of Instance-Based Collaborative Recommendation
Nicholas Kushmerick
81-90
i
Boost: Boosting Using an
i
nstance-Based Exponential Weighting Scheme
Stephen Kwek and Chau Nguyen
258-270
Towards a Simple Clustering Criterion Based on Minimum Length Encoding
Marcus-Christopher Ludl and Gerhard Widmer
167-185
Class Probability Estimation and Cost-Sensitive Classification Decisions
Dragos D. Margineantu
173-198
On-Line Support Vector Machine Regression
Mario Martin
187-195
Q-Cut—Dynamic Discovery of Sub-goals in Reinforcement Learning
Ishai Menache, Shie Mannor and Nahum Shimkin
9-21
A Multistrategy Approach to the Classification of Phases in Business Cycles
Katharina Morik and Stefan Rüping
319-331
A Robust Boosting Algorithm
Richard Nock and Patrice Lefaucheur
77-90
Case Exchange Strategies in Multiagent Learning
Santiago Ontanón and Enric Plaza
185-194
Inductive Confidence Machines for Regression
Harris Papadopoulos, Kostas Proedrou, Volodya Vovk and Alex Gammerman
139-149
Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique
Lourdes Pena Castillo and Stefan Wrobel
47-76
Propagation of Q-values in Tabular TD(λ)
Philippe Preux
221-231
Transductive Confidence Machines for Pattern Recognition
Kostas Proedrou, Ilia Nouretdinov, Volodya Vovk and Alex Gammerman
131-134
Characterizing Markov Decision Processes
Bohdana Ratitch and Doina Precup
43-63
Phase Transitions and Stochastic Local Search in k-Term DNF Learning
Ulrich Rückert, Stefan Kramer and Luc De Raedt
109-137
Discriminative Clustering: Optimal Contingency Tables by Learning Metrics
Janne Sinkkonen, Samuel Kaski and Janne Nikkilä
147-153
Boosting Density Function Estimators
Franck Thollard, Marc Sebban and Philippe Ezequel
123-137
Ranking with Predictive Clustering Trees
Ljupco Todorovski, Hendrik Blockeel and Saso Dzeroski
145-165
Support Vector Machines for Polycategorical Classification
Ioannis Tsochantaridis and Thomas Hofmann
69-78
Learning Classification with Both Labeled and Unlabeled Data
Jean-Noël Vittaut, Massih-Reza Amini and Patrick Gallinari
480-492
An Information Geometric Perspective on Active Learning
Chen-Hsiang Yeang
199-220
Stacking with an Extended Set of Meta-level Attributes and MLR
Bernard Zenko and Saso Dzeroski
91-103
Finding Hidden Factors Using Independent Component Analysis
Erkki Oja
39-58
Reasoning with Classifiers
Dan Roth
151-167
A Kernel Approach for Learning from almost Orthogonal Patterns
Bernhard Schölkopf, Jason Weston, Eleazar Eskin, Christina Leslie and William Stafford Noble
139-171
Learning with Mixture Models: Concepts and Applications
Padhraic Smyth
Back matter