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Evolving Connectionist Systems

Methods and Applications in Bioinformatics, Brain Study and Intelligent Machines

  • Book
  • © 2003

Overview

Authors:
  1. Nikola Kasabov

    1. University of Otago, Dunedin, New Zealand
      Auckland University of Technology, New Zealand

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  • The only book to provide a comprehensive, state-of-the-art overview of evolving connectionist systems
  • Includes supplementary material: sn.pub/extras

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

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Table of contents (14 chapters)

  1. Front Matter

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

    1. Front Matter

      Pages 163-163
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    2. Data Analysis, Modelling and Knowledge Discovery in Bioinformatics

      • Nikola Kasabov
      Pages 165-192

    3. Dynamic Modelling of Brain Functions and Cognitive Processes

      • Nikola Kasabov
      Pages 193-208

    4. Modelling the Emergence of Acoustic Segments (Phonemes) in Spoken Languages

      • Nikola Kasabov
      Pages 209-228

    5. On-Line Adaptive Speech Recognition

      • Nikola Kasabov
      Pages 229-244

    6. On-Line Image and Video Data Processing

      • Nikola Kasabov
      Pages 245-256

    7. Evolving Systems for Integrated Multi-Modal Information Processing

      • Nikola Kasabov
      Pages 257-271

    8. Epilogue

      • Nikola Kasabov
      Pages 273-273

  4. Back Matter

    Pages 275-307
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Keywords

About this book

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.

Authors and Affiliations

  • University of Otago, Dunedin, New Zealand

    Nikola Kasabov

  • Auckland University of Technology, New Zealand

    Nikola Kasabov

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