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Meta-Learning - Concepts and Techniques

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Data Mining and Knowledge Discovery Handbook

Summary

The field of meta-learning has as one of its primary goals the understanding of the interaction between the mechanism of learning and the concrete contexts in which that mechanism is applicable. The field has seen a continuous growth in the past years with interesting new developments in the construction of practical model-selection assistants, task-adaptive learners, and a solid conceptual framework. In this chapter we give an overview of different techniques necessary to build meta-learning systems. We begin by describing an idealized meta-learning architecture comprising a variety of relevant component techniques. We then look at how each technique has been studied and implemented by previous research. In addition we show how meta-learning has already been identified as an important component in real-world applications.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Houston, Houston, USA

    Ricardo Vilalta

  2. Department of Computer Science, Brigham Young University, Provo, Utah, USA

    Christophe Giraud-Carrier

  3. Faculty of Economics, University of Porto, Porto, Portugal

    Pavel Brazdil

Authors
  1. Ricardo Vilalta
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  2. Christophe Giraud-Carrier
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  3. Pavel Brazdil
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Editor information

Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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Vilalta, R., Giraud-Carrier, C., Brazdil, P. (2009). Meta-Learning - Concepts and Techniques. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_36

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_36

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  • Online ISBN: 978-0-387-09823-4

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