Machine Learning: ECML 2002

Contributed Papers.- Convergent Gradient Ascent in General-Sum Games.- Revising Engineering Models: Combining Computational Discovery with Knowledge.- Variational Extensions to EM and Multinomial PCA.- Learning and Inference for Clause Identification.- An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks.- Variance Optimized Bagging.- How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code.- Sparse Online Greedy Support Vector Regression.- Pairwise Classification as an Ensemble Technique.- RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood.- Using Hard Classifiers to Estimate Conditional Class Probabilities.- Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner.- Scaling Boosting by Margin-Based Inclusion of Features and Relations.- Multiclass Alternating Decision Trees.- Possibilistic Induction in Decision-Tree Learning.-Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains.- Collaborative Learning of Term-Based Concepts for Automatic Query Expansion.- Learning to Play a Highly Complex Game from Human Expert Games.- Reliable Classifications with Machine Learning.- Robustness Analyses of Instance-Based Collaborative Recommendation.- iBoost: Boosting Using an instance-Based Exponential Weighting Scheme.- Towards a Simple Clustering Criterion Based on Minimum Length Encoding.- Class Probability Estimation and Cost-Sensitive Classification Decisions.- On-Line Support Vector Machine Regression.- Q-Cut-Dynamic Discovery of Sub-goals in Reinforcement Learning.- A Multistrategy Approach to the Classification of Phases in Business Cycles.- A Robust Boosting Algorithm.- Case Exchange Strategies in Multiagent Learning.- Inductive Confidence Machines for Regression.- Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique.- Propagation of Q-values in Tabular TD(?).- Transductive Confidence Machines for Pattern Recognition.- Characterizing Markov Decision Processes.- Phase Transitions and Stochastic Local Search in k-Term DNF Learning.- Discriminative Clustering: Optimal Contingency Tables by Learning Metrics.- Boosting Density Function Estimators.- Ranking with Predictive Clustering Trees.- Support Vector Machines for Polycategorical Classification.- Learning Classification with Both Labeled and Unlabeled Data.- An Information Geometric Perspective on Active Learning.- Stacking with an Extended Set of Meta-level Attributes and MLR.- Invited Papers.- Finding Hidden Factors Using Independent Component Analysis.- Reasoning with Classifiers.- A Kernel Approach for Learning from almost Orthogonal Patterns.- Learning with Mixture Models: Concepts and Applications.