Meta-Learning in Computational Intelligence

  • Norbert Jankowski
  • Włodzisław Duch
  • Krzysztof Gra̧bczewski

Part of the Studies in Computational Intelligence book series (SCI, volume 358)

Table of contents

  1. Front Matter
  2. Norbert Jankowski, Krzysztof Grąbczewski
    Pages 1-76
  3. Kate A. Smith-Miles, Rafiqul M. D. Islam
    Pages 77-95
  4. Damien François, Vincent Wertz, Michel Verleysen
    Pages 97-115
  5. Ciro Castiello, Anna Maria Fanelli
    Pages 157-177
  6. Pavel Kordík, Jan Černý
    Pages 179-223
  7. Ricardo B. C. Prudêncio, Marcilio C. P. de Souto, Teresa B. Ludermir
    Pages 225-243
  8. Melanie Hilario, Phong Nguyen, Huyen Do, Adam Woznica, Alexandros Kalousis
    Pages 273-315
  9. Włodzisław Duch, Tomasz Maszczyk, Marek Grochowski
    Pages 317-358
  10. Back Matter

About this book

Introduction

Computational Intelligence (CI) community has developed hundreds of algorithms for intelligent data analysis, but still many hard problems in computer vision, signal processing or text and multimedia understanding, problems that require deep learning techniques, are open.
Modern data mining packages contain numerous modules for data acquisition, pre-processing, feature selection and construction, instance selection, classification, association and approximation methods, optimization techniques, pattern discovery, clusterization, visualization and post-processing. A large data mining package allows for billions of ways in which  these modules can be combined. No human expert can claim to explore and understand all possibilities in the knowledge discovery process.

This is where algorithms that learn how to learnl come to rescue.
Operating in the space of all available data transformations and optimization techniques these algorithms use meta-knowledge about learning processes automatically extracted from experience of solving diverse problems. Inferences about transformations useful in different contexts help to construct learning algorithms that can uncover various aspects of knowledge hidden in the data. Meta-learning shifts the focus of the whole CI field from individual learning algorithms to the higher level of learning how to learn.

This book defines and reveals new theoretical and practical trends in meta-learning, inspiring the readers to further research in this exciting field.

Keywords

Computational Intelligence Meta-learning

Editors and affiliations

  • Norbert Jankowski
    • 1
  • Włodzisław Duch
    • 1
  • Krzysztof Gra̧bczewski
    • 1
  1. 1.Department of InformaticsNicolaus Copernicus UniversityToruńPoland

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-20980-2
  • Copyright Information Springer Berlin Heidelberg 2011
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-20979-6
  • Online ISBN 978-3-642-20980-2
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book
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