Metalearning: Applications to Data Mining / Edition 1

Metalearning: Applications to Data Mining / Edition 1

by Pavel Brazdil, Christophe Giraud Carrier, Carlos Soares, Ricardo Vilalta
     
 

ISBN-10: 3540732624

ISBN-13: 9783540732624

Pub. Date: 10/22/2008

Publisher: Springer Berlin Heidelberg

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

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Overview

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.

This book discusses several approaches to obtaining knowledge concerning the performance of machine learning and data mining algorithms. It shows how this knowledge can be reused to select, combine, compose and adapt both algorithms and models to yield faster, more effective solutions to data mining problems. It can thus help developers improve their algorithms and also develop learning systems that can improve themselves.

The book will be of interest to researchers and graduate students in the areas of machine learning, data mining and artificial intelligence.

Product Details

ISBN-13:
9783540732624
Publisher:
Springer Berlin Heidelberg
Publication date:
10/22/2008
Series:
Cognitive Technologies Series
Edition description:
2009
Pages:
176
Product dimensions:
6.40(w) x 9.30(h) x 0.80(d)

Table of Contents

Metalearning: Concepts and Systems.- Metalearning for Algorithm Recommendation: an Introduction.- Development of Metalearning Systems for Algorithm Recommendation.- Extending Metalearning to Data Mining and KDD.- Extending Metalearning to Data Mining and KDD.- Bias Management in Time-Changing Data Streams.- Transfer of Metaknowledge Across Tasks.- Composition of Complex Systems: Role of Domain-Specific Metaknowledge.

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