Advances in Computational Intelligence and Learning

Methods and Applications

  • Editors
  • Hans-Jürgen Zimmermann
  • Georgios Tselentis
  • Maarten van Someren
  • Georgios Dounias

Part of the International Series in Intelligent Technologies book series (ISIT, volume 18)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Methodologies

    1. Front Matter
      Pages 1-1
    2. Robert Babuška
      Pages 3-16
    3. James C. Bezdek, Nikhil R. Pal, Thomas A. Runkler, Kuhu Pal
      Pages 17-41
    4. Luren Yang, Tom Kavli, Mats Carlin, Sigmund Clausen, Paul F. M. De Groot
      Pages 71-84
    5. Santiago Garrido, Luis Moreno, Miguel Angel Salichs
      Pages 107-116
    6. Marian B. Gorzalczany, Adam Gluszek
      Pages 135-146
    7. Liam P. Maguire, T. Martin McGinnity, Brendan P. Glackin
      Pages 147-158
    8. Maarten W. van Someren
      Pages 183-192
    9. G. Petasis, S. Petridis, G. Paliouras, V. Karkaletsis, S. J. Perantonis, C. D. Spyropoulos
      Pages 193-210
    10. Martin Appl, Wilfried Brauer
      Pages 211-223
    11. Christian Kuhn, Jürgen Wernstedt
      Pages 225-243
  3. Applications

    1. Front Matter
      Pages 245-245
    2. Christer Carlsson, Robert Fullér
      Pages 247-262

About this book

Introduction

Advances in Computational Intelligence and Learning: Methods and Applications presents new developments and applications in the area of Computational Intelligence, which essentially describes methods and approaches that mimic biologically intelligent behavior in order to solve problems that have been difficult to solve by classical mathematics. Generally Fuzzy Technology, Artificial Neural Nets and Evolutionary Computing are considered to be such approaches.

The Editors have assembled new contributions in the areas of fuzzy sets, neural sets and machine learning, as well as combinations of them (so called hybrid methods) in the first part of the book. The second part of the book is dedicated to applications in the areas that are considered to be most relevant to Computational Intelligence.

Keywords

Pattern Recognition algorithms classification cognition computational intelligence data mining evolution evolutionary algorithm evolutionary computation genetic algorithm machine learning modeling neural network optimization reinforcement learning

Bibliographic information

  • DOI https://doi.org/10.1007/978-94-010-0324-7
  • Copyright Information Kluwer Academic Publishers 2002
  • Publisher Name Springer, Dordrecht
  • eBook Packages Springer Book Archive
  • Print ISBN 978-94-010-3872-0
  • Online ISBN 978-94-010-0324-7
  • Series Print ISSN 1382-3434
  • About this book
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