Metalearning: Applications to Data Mining / Edition 1

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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.
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Editorial Reviews

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"There are many techniques available for machine learning from data … . the problem is: given a set of data, which of the learning systems should one use? The goal of this book is to initiate a study of this problem. … The mixture of detailed description and overview is well managed. The reader is able to see how the authors’ ideas and work fit into a larger framework. Graduate students looking for thesis topics should read this book." (J. P. E. Hodgson, ACM Computing Reviews, May, 2009)

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Product Details

  • ISBN-13: 9783540732624
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 10/22/2008
  • Series: Cognitive Technologies Series
  • Edition description: 2009
  • Edition number: 1
  • Pages: 176
  • Product dimensions: 6.40 (w) x 9.30 (h) x 0.80 (d)

Table of 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

References 153

A Terminology 171

B Mathematical Symbols 173

Index 175

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