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Evolving Connectionist Systems

Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines

  • Nikola Kasabov
Book

Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)

Table of contents

  1. Front Matter
    Pages i-3
  2. Evolving Connectionist Systems: Methods and Techniques

  3. Evolving Connectionist Systems: Applications in Bioinformatics, Brain Study, and Intelligent Systems

    1. Front Matter
      Pages 163-163
    2. Nikola Kasabov
      Pages 229-244
    3. Nikola Kasabov
      Pages 245-256
    4. Nikola Kasabov
      Pages 273-273
  4. Back Matter
    Pages 275-307

About this book

Introduction

Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.

Keywords

automata bioinformatics cognition data analysis evolution evolutionary computation fuzzy logic image analysis information processing knowledge discovery learning modeling reinforcement learning speech recognition supervised learning

Authors and affiliations

  • Nikola Kasabov
    • 1
    • 2
  1. 1.University of OtagoDunedinNew Zealand
  2. 2.Auckland University of TechnologyNew Zealand

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4471-3740-5
  • Copyright Information Springer-Verlag London 2003
  • Publisher Name Springer, London
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-85233-400-0
  • Online ISBN 978-1-4471-3740-5
  • Series Print ISSN 1431-6854
  • Buy this book on publisher's site
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