© 2009

Foundations of Computational, Intelligence Volume 1

Learning and Approximation

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


  • First volume of a Reference work on the foundations of computational intelligence

  • Devoted to learning and approximation


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

Table of contents

  1. Front Matter
  2. Function Approximation

    1. Front Matter
      Pages 1-1
    2. Christopher Fogelberg, Vasile Palade
      Pages 3-34
    3. Dirk Gorissen, Karel Crombecq, Ivo Couckuyt, Tom Dhaene
      Pages 35-62
    4. Marcelo Costa, Thiago Rodrigues, Euler Horta, Antônio Braga, Carmen Pataro, René Natowicz et al.
      Pages 63-82
    5. Ruxandra Stoean, Mike Preuss, Catalin Stoean, Elia El-Darzi, D. Dumitrescu
      Pages 83-114
  3. Connectionist Learning

    1. Front Matter
      Pages 115-115
    2. M. Hadi Mashinchi, Siti Mariyam H. J. Shamsuddin
      Pages 143-158
    3. Cícero Nogueira dos Santos, Ruy Luiz Milidiú
      Pages 159-184
    4. Arturo Chavoya
      Pages 185-215
    5. Moumen T. El-Melegy, Mohammed H. Essai, Amer A. Ali
      Pages 217-242
    6. Paolo Renna, Pierluigi Argoneto
      Pages 243-277
  4. Knowledge Representation and Acquisition

    1. Front Matter
      Pages 279-279
    2. Daswin De Silva, Damminda Alahakoon, Shyamali Dharmage
      Pages 281-305
    3. Hazem M. El-Bakry, Mohamed Hamada
      Pages 307-330
  5. Learning and Visualization

    1. Front Matter
      Pages 331-331
    2. Sébastien Aupetit, Nicolas Monmarché, Pierre Liardet, Mohamed Slimane
      Pages 333-361
    3. Arthur L. Hsu, Isaam Saeed, Saman K. Halgamuge
      Pages 363-379
    4. Sultan Noman, Siti Mariyam Shamsuddin, Aboul Ella Hassanien
      Pages 381-397

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.


Approximation Computational Intelligence Evolution Fuzzy algorithms calculus genome intelligence knowledge representation learning machine learning neural network optimization supervised learning visualization

Editors and affiliations

  • Aboul-Ella Hassanien
    • 1
  • Ajith Abraham
    • 2
  • Athanasios V. Vasilakos
    • 3
  • Witold Pedrycz
    • 4
  1. 1.College of Business Administration, Quantitative and Information System DepartmentKuwait UniversitySafatKuwait
  2. 2.Center of Excellence for Quantifiable, Quality of ServiceNorwegian University of Science & TechnologyTrondheimNorway
  3. 3.Department of Computer and Telecommunications EngineeringUniversity ofWestern MacedoniaKozaniGreece
  4. 4.Dept. Electrical and Computer EngineeringUniversity of AlbertaEdmonton,AlbertaCanada

Bibliographic information

  • Book Title Foundations of Computational, Intelligence Volume 1
  • Book Subtitle Learning and Approximation
  • Editors Aboul-Ella Hassanien
    Ajith Abraham
    Athanasios V. Vasilakos
    Witold Pedrycz
  • Series Title Studies in Computational Intelligence
  • DOI
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering Engineering (R0)
  • Hardcover ISBN 978-3-642-01081-1
  • Softcover ISBN 978-3-662-56843-9
  • eBook ISBN 978-3-642-01082-8
  • Series ISSN 1860-949X
  • Series E-ISSN 1860-9503
  • Edition Number 1
  • Number of Pages XII, 400
  • Number of Illustrations 0 b/w illustrations, 0 illustrations in colour
  • Topics Artificial Intelligence
    Mathematical and Computational Engineering
  • Buy this book on publisher's site
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