Pattern Recognition and Machine Learning

1. Front Matter

   Pages i-ix

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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

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

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

  K. S. Fu

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