Metalearning

Applications to Data Mining

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

Metalearning Cover Image

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.

Full Description

  • ISBN13: 978-3-5407-3262-4
  • 192 Pages
  • User Level: Students
  • Publication Date: November 18, 2008
  • Available eBook Formats: PDF
  • eBook Price: $89.95
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Full Description
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.
Table of Contents

Table of Contents

  1. Metalearning
  2. Concepts and Architectures.
  3. Metalearning for Algorithm Recommendation.
  4. Advanced Issues on Metalearning for Algorithm Recommendation.
  5. Combining Base Learners.
  6. Extending Metalearning to Data Mining and KDD.
  7. Adaptive Learning.
  8. Transfer of (Meta)knowledge Across Tasks.
  9. Composition of Systems and Applications.
  10. Lessons Learned and Future Work.
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