Metalearning

Applications to Data Mining

  • Pavel Brazdil
  • Christophe Giraud-Carrier
  • Carlos Soares
  • Ricardo Vilalta

Part of the Cognitive Technologies book series (COGTECH)

About this book

Introduction

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.

Keywords

DOM algorithms artificial intelligence complex system data mining intelligence knowledge learning machine learning

Authors and affiliations

  • Pavel Brazdil
    • 1
  • Christophe Giraud-Carrier
    • 2
  • Carlos Soares
    • 1
  • Ricardo Vilalta
    • 3
  1. 1.LIAADUniversidade do PortoPortoPortugal
  2. 2.Department of Computer ScienceBrigham Young UniversityProvoUSA
  3. 3.Department of Computer ScienceUniversity of HoustonHoustonUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-73263-1
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Computer Science
  • Print ISBN 978-3-540-73262-4
  • Online ISBN 978-3-540-73263-1
  • Series Print ISSN 1611-2482
  • About this book