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Foundations of Computational Intelligence

Volume 1: Learning and Approximation

  • Book
  • © 2009

Overview

Editors:
  1. Aboul-Ella Hassanien

    1. Information Technology Department, Cairo University Faculty of Computers and Information, Orman, Giza, Egypt

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  2. Ajith Abraham

    1. Aster 13 C, Skyline Apartments North Fort Gate, Petah, Tripunithura, India

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  3. Athanasios V. Vasilakos

    1. N.Erythraia, Greece

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  4. Witold Pedrycz

    1. University of Alberta Dept. Electrical & Computer Engineering, Edmonton, Canada

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  • First volume of a Reference work on the foundations of computational intelligence
  • Devoted to learning and approximation

Part of the book series: Studies in Computational Intelligence (SCI, volume 201)

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Keywords

  • Approximation
  • Computational Intelligence
  • Learning

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.

Editors and Affiliations

  • Information Technology Department, Cairo University Faculty of Computers and Information, Orman, Giza, Egypt

    Aboul-Ella Hassanien

  • Aster 13 C, Skyline Apartments North Fort Gate, Petah, Tripunithura, India

    Ajith Abraham

  • N.Erythraia, Greece

    Athanasios V. Vasilakos

  • University of Alberta Dept. Electrical & Computer Engineering, Edmonton, Canada

    Witold Pedrycz

Bibliographic Information

  • Book Title: Foundations of Computational Intelligence

  • Book Subtitle: Volume 1: Learning and Approximation

  • Editors: Aboul-Ella Hassanien, Ajith Abraham, Athanasios V. Vasilakos, Witold Pedrycz

  • Series Title: Studies in Computational Intelligence

  • Publisher: Springer Berlin, Heidelberg

  • Copyright Information: Springer Berlin Heidelberg 2009

  • Softcover ISBN: 978-3-642-10164-9Published: 28 October 2010

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 400

  • Number of Illustrations: 126 b/w illustrations

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