Metalearning : applications to data mining / Pavel Brazdil ... [and others].

Format:

Book

Contributor:

Brazdil, Pavel B., 1945-

Series:

Cognitive technologies

Cognitive technologies, 1611-2482

Language:

English

Subjects (All):

Machine learning.

Data mining.

Physical Description:

x, 176 pages : illustrations ; 25 cm.

Place of Publication:

Berlin : Springer, [2009]

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.

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.

Contents:

1 Metalearning: Concepts and Systems 1

2 Metalearning for Algorithm Recommendation: an Introduction 11

3 Development of Metalearning Systems for Algorithm Recommendation 31

4 Extending Metalearning to Data Mining and KDD 61

5 Combining Base-Learners 73

6 Bias Management in Time-Changing Data Streams 91

7 Transfer of Metaknowledge Across Tasks 109

8 Composition of Complex Systems: Role of Domain-Specific Metaknowledge 129

B Mathematical Symbols 173.

Notes:

"With 53 figures and 11 tables."

Includes bibliographical references (pages [153]-169) and index.

ISBN:

9783540732624

3540732632

9783540732631

3540732624

OCLC:

298595059