Studies in Computational Intelligence

Foundations of Computational Intelligence

Volume 1: Learning and Approximation

Editors: Hassanien, A.-E., Abraham, A., Vasilakos, A.V., Pedrycz, W. (Eds.)

  • This is the first volume of a reference work on the foundations of Computational Intelligence
  • This volume is devoted to learning and approximation
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Softcover $179.00
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  • ISBN 978-3-642-10164-9
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About this book

Learning methods and approximation algorithms are fundamental tools that deal with computationally hard problems and problems in which the input is gradually disclosed over time. Both kinds of problems have a large number of applications arising from a variety of fields, such as algorithmic game theory, approximation classes, coloring and partitioning, competitive analysis, computational finance, cuts and connectivity, geometric problems, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approximation and online algorithms, randomization techniques, real-world applications, scheduling problems and so on. The past years have witnessed a large number of interesting applications using various techniques of Computational Intelligence such as rough sets, connectionist learning; fuzzy logic; evolutionary computing; artificial immune systems; swarm intelligence; reinforcement learning, intelligent multimedia processing etc.. In spite of numerous successful applications of Computational Intelligence in business and industry, it is sometimes difficult to explain the performance of these techniques and algorithms from a theoretical perspective. Therefore, we encouraged authors to present original ideas dealing with the incorporation of different mechanisms of Computational Intelligent dealing with Learning and Approximation algorithms and underlying processes.

This edited volume comprises 15 chapters, including an overview chapter, which provides an up-to-date and state-of-the art research on the application of Computational Intelligence for learning and approximation.

Table of contents (15 chapters)

  • Machine Learning and Genetic Regulatory Networks: A Review and a Roadmap

    Fogelberg, Christopher (et al.)

    Pages 3-34

  • Automatic Approximation of Expensive Functions with Active Learning

    Gorissen, Dirk (et al.)

    Pages 35-62

  • New Multi-Objective Algorithms for Neural Network Training Applied to Genomic Classification Data

    Costa, Marcelo (et al.)

    Pages 63-82

  • An Evolutionary Approximation for the Coefficients of Decision Functions within a Support Vector Machine Learning Strategy

    Stoean, Ruxandra (et al.)

    Pages 83-114

  • Meta-learning and Neurocomputing – A New Perspective for Computational Intelligence

    Castiello, Ciro

    Pages 117-142

Buy this book

Softcover $179.00
price for USA
  • ISBN 978-3-642-10164-9
  • Free shipping for individuals worldwide
  • Usually dispatched within 3 to 5 business days.

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Bibliographic Information

Bibliographic Information
Book Title
Foundations of Computational Intelligence
Book Subtitle
Volume 1: Learning and Approximation
  • Aboul-Ella Hassanien
  • Ajith Abraham
  • Athanasios V. Vasilakos
  • Witold Pedrycz
Series Title
Studies in Computational Intelligence
Series Volume
Springer-Verlag Berlin Heidelberg
Copyright Holder
Springer Berlin Heidelberg
Softcover ISBN
Series ISSN
Edition Number
Number of Pages
XII, 400
Number of Illustrations and Tables
126 b/w illustrations