机器学习：ECML2002／会议录Machine learning(机器学习：ECML2002／会议录)

The LNAI series reports state-of-the-art results in artificial intelligence re-search，development，and education，at a high level and in both printed and electronic form. Enjoying tight cooperation with the R＆D community，with numerous individuals，as well as with prestigious organizations and societies，LNAI has grown into the most comprehensive artificial intelligence research forum available.
The scope of LNAI spans the whole range of artificial intelligence and intelli-gent information processing including interdisciplinary topics in a variety of
application fields. The type of material published traditionally includes
proceedings（published in time for the respective conference）
post-proceedings（consisting of thoroughly revised final full papers）
research monographs（which may be based on PhD work）

Contributed Papers
Convergent Gradient Ascent in General-Sum Games
Revising Engineering Models: Combining Computational Discovery
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
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 Matja2 Kukar and Igor Kononenko
　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
……
Invited Papers
Author Index