Machine Learning: ECML 2001: 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001. Proceedings [Paperback]

This book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001.
The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning.Regular Papers.- An Axiomatic Approach to Feature Term Generalization.- Lazy Induction of Descriptions for Relational Case-Based Learning.- Estimating the Predictive Accuracy of a Classifier.- Improving the Robustness and Encoding Complexity of Behavioural Clones.- A Framework for Learning Rules from Multiple Instance Data.- Wrapping Web Information Providers by Transducer Induction.- Learning While Exploring: Bridging the Gaps in the Eligibility Traces.- A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Holdem Poker.- Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner.- Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example.- Iterative Double Clustering for Unsupervised and Semi-supervised Learning.- On the Practice of Branching Program Boosting.- A Simple Approach to Ordinal Classification.- Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable,Continuous Optimization Problem.- Extraction of Recurrent Patterns from Stratified Ordered Trees.- Understanding Probabilistic Classifiers.- Efficiently Determining the Starting Sample Size for Progressive Sampling.- Using Subclasses to Improve Classification Learning.- Learning What People (Dont) Want.- TowardlC�