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Pattern Recognition and Machine Learning

Proceedings of the Japan—U.S. Seminar on the Learning Process in Control Systems, held in Nagoya, Japan August 18–20, 1970

  • Conference proceedings
  • © 1971

Overview

Editors:
  1. K. S. Fu

    1. School of Electrical Engineering, Purdue University, Lafayette, USA

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Table of contents (28 papers)

  1. Front Matter

    Pages i-ix
    Download chapter PDF

  2. Pattern Recognition and System Identification

    1. Some Studies on Pattern Recognition with Nonsupervised Learning Procedures

      • Kokichi Tanaka
      Pages 1-17

    2. Linear and Nonlinear Stochastic Approximation Algorithms for Learning Systems

      • Y. T. Chien
      Pages 18-28

    3. Multi-Category Pattern Classification Using a Nonsupervised Learning Algorithm

      • Iwao Morishita, Ryuji Takanuki
      Pages 29-41

    4. A Mixed-Type Non-Parametric Learning Machine without a Teacher

      • Masamichi Shimura
      Pages 42-55

    5. Recognition System for Handwritten Letters Simulating Visual Nervous System

      • Katsuhiko Fujii, Tatsuya Morita
      Pages 56-69

    6. Sequential Identification by Means of Gradient Learning Algorithms

      • J. M. Mendel
      Pages 70-78

    7. Stochastic Approximation Algorithms for System Identification Using Normal Operating Data

      • Yasuyuki Funahashi, Kahei Nakamura
      Pages 79-86

    8. On Utilization of Structural Information to Improve Identification Accuracy

      • M. Aoki, P. C. Yue
      Pages 87-96

    9. An Inconsistency between the Rate and the Accuracy of the Learning Method for System Identification and its Tracking Characteristics

      • Atsuhiko Noda
      Pages 97-110

    10. Weighting Function Estimation in Distributed-Parameter Systems

      • Henry E. Lee, D. W. C. Shen
      Pages 111-120

    11. System Identifications by a Nonlinear Filter

      • Setsuzo Tsuji, Kousuke Kumamaru
      Pages 121-137

    12. A Linear Filter for Discrete Systems with Correlated Measurement Noise

      • Tzyh Jong Tarn, John Zaborszky
      Pages 138-149

  3. Learning Process and Learning Control

    1. Stochastic Learning by Means of Controlled Stochastic Processes

      • Seigo Kano
      Pages 150-159

    2. Learning Processes in a Random Machine

      • Sadamu Ohteru, Tomokazu Kato, Yoshiyuki Nishihara, Yasuo Kinouchi
      Pages 160-171

    3. Learning Process in a Model of Associative Memory

      • Kaoru Nakano
      Pages 172-186

    4. Adaptive Optimization in Learning Control

      • George J. McMurtry
      Pages 187-194

    5. Learning Control of Multimodal Systems by Fuzzy Automata

      • Kiyoji Asai, Seizo Kitajima
      Pages 195-203

    6. On a Class of Performance-Adaptive Self-Organizing Control Systems

      • George N. Saridis
      Pages 204-220

    7. A Control System Improving its Control Dynamics by Learning

      • Kahei Nakamura
      Pages 221-229
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Keywords

About this book

This book contains the Proceedings of the US-Japan Seminar on Learning Process in Control Systems. The seminar, held in Nagoya, Japan, from August 18 to 20, 1970, was sponsored by the US-Japan Cooperative Science Program, jointly supported by the National Science Foundation and the Japan Society for the Promotion of Science. The full texts of all the presented papers except two t are included. The papers cover a great variety of topics related to learning processes and systems, ranging from pattern recognition to systems identification, from learning control to biological modelling. In order to reflect the actual content of the book, the present title was selected. All the twenty-eight papers are roughly divided into two parts--Pattern Recognition and System Identification and Learning Process and Learning Control. It is sometimes quite obvious that some papers can be classified into either part. The choice in these cases was strictly the editor's in order to keep a certain balance between the two parts. During the past decade there has been a considerable growth of interest in problems of pattern recognition and machine learn­ ing. In designing an optimal pattern recognition or control system, if all the a priori information about the process under study is known and can be described deterministically, the optimal system is usually designed by deterministic optimization techniques.

Editors and Affiliations

  • School of Electrical Engineering, Purdue University, Lafayette, USA

    K. S. Fu

Bibliographic Information

  • Book Title: Pattern Recognition and Machine Learning

  • Book Subtitle: Proceedings of the Japan—U.S. Seminar on the Learning Process in Control Systems, held in Nagoya, Japan August 18–20, 1970

  • Editors: K. S. Fu

  • DOI: https://doi.org/10.1007/978-1-4615-7566-5

  • Publisher: Springer New York, NY

  • eBook Packages: Springer Book Archive

  • Copyright Information: Plenum Press, New York 1971

  • Softcover ISBN: 978-1-4615-7568-9Published: 12 December 2012

  • eBook ISBN: 978-1-4615-7566-5Published: 06 December 2012

  • Edition Number: 1

  • Number of Pages: X, 344

  • Topics: Pattern Recognition, Artificial Intelligence, Computer Applications in Chemistry

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