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Foundations of Computational Intelligence

Volume 1: Learning and Approximation

  • Book
  • © 2009

Overview

  • 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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Table of contents (15 chapters)

  1. Function Approximation

  2. Connectionist Learning

  3. Knowledge Representation and Acquisition

  4. Learning and Visualization

Keywords

About this book

Foundations of Computational Intelligence Volume 1: Learning and Approximation: Theoretical Foundations and Applications 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, inapproximability results, mechanism design, network design, packing and covering, paradigms for design and analysis of approxi- tion 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 C- putational 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 inc- poration of different mechanisms of Computational Intelligent dealing with Lea- ing 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

  • College of Business Administration, Quantitative and Information System Department, Kuwait University, Safat, Kuwait

    Aboul-Ella Hassanien

  • Center of Excellence for Quantifiable, Quality of Service, Norwegian University of Science & Technology, Trondheim, Norway

    Ajith Abraham

  • Department of Computer and Telecommunications Engineering, University ofWestern Macedonia, Kozani, Greece

    Athanasios V. Vasilakos

  • Dept. Electrical and Computer Engineering, University of Alberta, Edmonton,Alberta, 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

  • DOI: https://doi.org/10.1007/978-3-642-01082-8

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2009

  • Hardcover ISBN: 978-3-642-01081-1Published: 06 May 2009

  • Softcover ISBN: 978-3-662-56843-9Published: 28 March 2019

  • eBook ISBN: 978-3-642-01082-8Published: 02 May 2009

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: XII, 400

  • Topics: Artificial Intelligence, Mathematical and Computational Engineering

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