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New Learning Paradigms in Soft Computing

  • Book
  • © 2002

Overview

Editors:
  1. Lakhmi C. Jain

    1. Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Adelaide, Mawson Lakes, Australia

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    You can also search for this editor in PubMed   Google Scholar

  2. Janusz Kacprzyk

    1. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

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    You can also search for this editor in PubMed   Google Scholar

  • Shows the use of fuzzy logic, neural networks and evoluationary computations in various machine learning procedures
  • Presents new trends in machine learning

Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ, volume 84)

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Table of contents (15 chapters)

  1. Front Matter

    Pages I-XII
    Download chapter PDF

  2. Statistical Learning by Natural Gradient Descent

    • H. H. Yang, S. Amari
    Pages 1-29

  3. Granular Networks and Granular Learning

    • Witold Pedrycz
    Pages 30-54

  4. Learning and Decision-Making in the Framework of Fuzzy Lattices

    • V. G. Kaburlasos, V. Petridis
    Pages 55-96

  5. Lazy Learning: A Logical Method for Supervised Learning

    • G. Bontempi, M. Birattari, H. Bersini
    Pages 97-136

  6. Active Learning in Neural Networks

    • M. Hasenjäger, H. Ritter
    Pages 137-169

  7. Knowledge Extraction from Reinforcement Learning

    • Ron Sun
    Pages 170-180

  8. Reinforcement Learning for Fuzzy Agents: Application to a Pighouse Environment Control

    • Lionel Jouffe
    Pages 181-230

  9. Performance Comparisons of Neural Networks and Machine Learning Techniques: A Critical Assessment of the Methodology

    • Achim Hoffmann
    Pages 231-250

  10. Digital Systems Design Through Learning

    • Witold Pedrycz, Piotr S. Szczepaniak
    Pages 251-275

  11. Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms

    • Krzysztof J. Cios, Łukasz A. Kurgan
    Pages 276-322

  12. An Integer Programming Approach to Inductive Learning Using Genetic and Greedy Algorithms

    • Janusz Kacprzyk, Grażyna Szkatuła
    Pages 323-367

  13. Using Unlabeled Data for Learning Classification Problems

    • A. Verikas, A. Gelzinis, K. Malmqvist
    Pages 368-403

  14. Problems of Rule Induction from Preterm Birth Data

    • Jerzy W. Grzymała-Busse, Linda K. Goodwin, Witold J. Grzymała-Busse, Xinqun Zheng
    Pages 404-418

  15. Reduction of Discriminant Rules Based on Frequent Item Set Calculation

    • María C. Fernández-Baizán, Ernestina Menasalvas Ruiz, Juan Francisco Martínez Sarrías
    Pages 419-438

  16. Deriving a Concise Description of Non-Self Patterns in an Artificial Immune System

    • Sławomir T. Wierzchoń
    Pages 439-464
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Keywords

About this book

Learning is a key issue in the analysis and design of all kinds of intelligent systems. In recent time many new paradigms of automated (machine) learning have been proposed in the literature. Soft computing, that has proved to be an effective and efficient tool in so many areas of science and technology, seems to offer new qualities in the realm of machine learning too. The purpose of this volume is to present some new learning paradigms that have been triggered, or at least strongly influenced by soft computing tools and techniques, mainly related to neural networks, fuzzy logic, rough sets, and evolutionary computations.

Editors and Affiliations

  • Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Adelaide, Mawson Lakes, Australia

    Lakhmi C. Jain

  • Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

Bibliographic Information

  • Book Title: New Learning Paradigms in Soft Computing

  • Editors: Lakhmi C. Jain, Janusz Kacprzyk

  • Series Title: Studies in Fuzziness and Soft Computing

  • DOI: https://doi.org/10.1007/978-3-7908-1803-1

  • Publisher: Physica Heidelberg

  • eBook Packages: Springer Book Archive

  • Copyright Information: Springer-Verlag Berlin Heidelberg 2002

  • Hardcover ISBN: 978-3-7908-1436-1Published: 14 December 2001

  • Softcover ISBN: 978-3-7908-2499-5Published: 09 October 2011

  • eBook ISBN: 978-3-7908-1803-1Published: 05 June 2013

  • Series ISSN: 1434-9922

  • Series E-ISSN: 1860-0808

  • Edition Number: 1

  • Number of Pages: XII, 464

  • Topics: Artificial Intelligence, Computational Intelligence

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