Metalearning

by Brazdil, Pavel; Giraud-carrier, Christophe; Soares, Carlos; Vilalta, Ricardo

• ISBN13: 9783540732624
• ISBN10: 3540732624
• Format: Hardcover
• Copyright: 2008-12-04
• Publisher: Springer-Verlag New York Inc

Summary

Metalearning is the study of principled methods that exploit metaknowledge to obtain efficient models and solutions by adapting machine learning and data mining processes. While the variety of machine learning and data mining techniques now available can, in principle, provide good model solutions, a methodology is still needed to guide the search for the most appropriate model in an efficient way. Metalearning provides one such methodology that allows systems to become more effective through experience.

Table of Contents

Metalearning: Concepts and Systems	p. 1
Metalearning for Algorithm Recommendation: an Introduction	p. 11
Development of Metalearning Systems for Algorithm Recommendation	p. 31
Extending Metalearning to Data Mining and KDD	p. 61
Combining Base-Learners	p. 73
Bias Management in Time-Changing Data Streams	p. 91
Transfer of Metaknowledge Across Tasks	p. 109
Composition of Complex Systems: Role of Domain-Specific Metaknowledge	p. 129
References	p. 153
Terminology	p. 171
Mathematical Symbols	p. 173
Index	p. 175
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