Machine Learning: ECML 2002
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Author Description
This book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002. The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.
Menrva's Summary
THE THESIS
This volume presents the cutting-edge research from the 13th European Conference on Machine Learning, ECML 2002. The proceedings compile 41 peer-reviewed full papers and 4 invited contributions, carefully selected from a vast number of submissions. The papers cover a wide spectrum of machine learning topics, reflecting the state-of-the-art in computational discovery, search strategies, and various learning paradigms. The collection aims to advance the field by disseminating novel algorithms, theoretical insights, and practical applications.
THE SCOPE MAP
- Computational discovery and search strategies
- Classification techniques, including support vector machines and kernel methods
- Rule induction, linear learning, and decision tree learning
- Boosting, collaborative learning, and statistical learning approaches
- Clustering, instance-based learning, and reinforcement learning
- Multiagent learning, multirelational learning, and Markov decision processes
- Active learning methodologies
THE AUTHORITY PERSPECTIVE
This collection of papers from ECML 2002 is regarded as a significant snapshot of the machine learning landscape at the time. The breadth of topics, from foundational algorithms like decision trees and support vector machines to more advanced areas such as multiagent and multirelational learning, indicates a comprehensive survey of contemporary research. The inclusion of invited contributions suggests an effort to highlight key trends and future directions within the field. The rigorous peer-review process implies that the presented work meets high academic standards.
Page Count:
538
Publication Date:
2002-01-01
Publisher:
Springer Nature
ISBN-10:
3540367551
ISBN-13:
9783540367550
Science
Non-Fiction
Algorithms
Data Mining
Computer Science
Machine Learning
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