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Computational Intelligence Paradigms

Innovative Applications

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  • © 2008

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Editors:
  1. Lakhmi C. Jain
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  2. Mika Sato-Ilic
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  3. Maria Virvou
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  4. George A. Tsihrintzis
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  5. Valentina Emilia Balas
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  6. Canicious Abeynayake
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  • Presents innovative applications of advanced computational intelligence paradigms

Part of the book series: Studies in Computational Intelligence (SCI, volume 137)

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Table of contents (12 chapters)

  1. Front Matter

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  2. An Introduction to Computational Intelligence Paradigms

    • Lakhmi C. Jain, Shing Chiang Tan, Chee Peng Lim
    Pages 1-23

  3. A Quest for Adaptable and Interpretable Architectures of Computational Intelligence

    • Witold Pedrycz
    Pages 25-49

  4. MembershipMap: A Data Transformation for Knowledge Discovery Based on Granulation and Fuzzy Membership Aggregation

    • Hichem Frigui
    Pages 51-76

  5. Advanced Developments and Applications of the Fuzzy ARTMAP Neural Network in Pattern Classification

    • Boaz Lerner, Hugo Guterman
    Pages 77-107

  6. Large Margin Methods for Structured Output Prediction

    • Elisa Ricci, Renzo Perfetti
    Pages 109-132

  7. Ensemble MLP Classifier Design

    • Terry Windeatt
    Pages 133-147

  8. Functional Principal Points and Functional Cluster Analysis

    • Nobuo Shimizu, Masahiro Mizuta
    Pages 149-165

  9. Clustering with Size Constraints

    • Frank Höppner, Frank Klawonn
    Pages 167-180

  10. Cluster Validating Techniques in the Presence of Duplicates

    • Ravi Jain, Andy Koronios
    Pages 181-193

  11. Fuzzy Blocking Regression Models

    • Mika Sato-Ilic
    Pages 195-217

  12. Support Vector Machines and Features for Environment Perception in Mobile Robotics

    • Rui Araújo, Urbano Nunes, Luciano Oliveira, Pedro Sousa, Paulo Peixoto
    Pages 219-250

  13. Linkage Analysis in Genetic Algorithms

    • Miwako Tsuji, Masaharu Munetomo
    Pages 251-279

  14. Back Matter

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Keywords

About this book

System designers are faced with a large set of data which has to be analysed and processed efficiently. Advanced computational intelligence paradigms present tremendous advantages by offering capabilities such as learning, generalisation and robustness. These capabilities help in designing complex systems which are intelligent and robust.

The book includes a sample of research on the innovative applications of advanced computational intelligence paradigms. The characteristics of computational intelligence paradigms such as learning, generalization based on learned knowledge, knowledge extraction from imprecise and incomplete data are the extremely important for the implementation of intelligent machines. The chapters include architectures of computational intelligence paradigms, knowledge discovery, pattern classification, clusters, support vector machines and gene linkage analysis. We believe that the research on computational intelligence will simulate great interest among designers and researchers of complex systems. It is important to use the fusion of various constituents of computational intelligence to offset the demerits of one paradigm by the merits of another.

Bibliographic Information

  • Book Title: Computational Intelligence Paradigms

  • Book Subtitle: Innovative Applications

  • Editors: Lakhmi C. Jain, Mika Sato-Ilic, Maria Virvou, George A. Tsihrintzis, Valentina Emilia Balas, Canicious Abeynayake

  • Series Title: Studies in Computational Intelligence

  • DOI: https://doi.org/10.1007/978-3-540-79474-5

  • Publisher: Springer Berlin, Heidelberg

  • eBook Packages: Engineering, Engineering (R0)

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2008

  • Hardcover ISBN: 978-3-540-79473-8Published: 14 June 2008

  • Softcover ISBN: 978-3-642-09839-0Published: 30 November 2010

  • eBook ISBN: 978-3-540-79474-5Published: 17 June 2008

  • Series ISSN: 1860-949X

  • Series E-ISSN: 1860-9503

  • Edition Number: 1

  • Number of Pages: VIII, 281

  • Topics: Mathematical and Computational Engineering, Artificial Intelligence

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