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

  • K. S. Fu

Table of contents

  1. Front Matter
    Pages i-ix
  2. Pattern Recognition and System Identification

  3. Learning Process and Learning Control

    1. Sadamu Ohteru, Tomokazu Kato, Yoshiyuki Nishihara, Yasuo Kinouchi
      Pages 160-171
    2. George J. McMurtry
      Pages 187-194
    3. Kiyoji Asai, Seizo Kitajima
      Pages 195-203
    4. Hiroshi Tamura
      Pages 230-242
    5. Shigeru Eiho, Bunji Kondo
      Pages 252-262
    6. R. Viswanathan, Kumpati S. Narendra
      Pages 277-287
  4. Back Matter
    Pages 337-343

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.


machine learning pattern pattern recognition

Editors and affiliations

  • K. S. Fu
    • 1
  1. 1.School of Electrical EngineeringPurdue UniversityLafayetteUSA

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