Adaptive, Learning, and Pattern Recognition Systems; theory and applications
Published on 28. February 1970
441 pages
978-0-08-095575-9 (ISBN)
Description
Adaptive, Learning, and Pattern Recognition Systems; theory and applications
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Jerry M. Mendel | King-Sun Fu
Adaptive Learning and Pattern Recognition Systems
Book
12/1970
Academic Press
€53.23
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Content
- Front Cover
- Adaptive, Learning and Pattern Recognition Systems: Theory and Applications
- Copyright Page
- Contents
- List of Contributors
- Preface
- PART I: PATTERN RECOGNITION
- Chapter 1. Elements of Pattern Recognition
- I. Introduction
- II. A Recognition Problem
- III. The Classical Model
- IV. Additions to the Classical Model
- References
- Chapter 2. Statistical Pattern Recognition
- I. Statistical Pattern Recognition Systems and Bayes Classifiers
- II. Sequential Decision Model for Pattern Classification
- III. Forward Sequential Classification Procedure with Time-Varying Stopping Boundaries
- IV. Backward Sequential Classification Procedure Using Dynamic Programming
- V. Backward Sequential Procedure for Both Feature Selection and Pattern Classification
- VI. Feature Selection and Ordering: Information Theoretic Approach
- VII. Feature Selection and Ordering: Karhunen-Loève Expansìon
- VIII. Bayesian Estimation in Statistical Classification Systems
- IX. Nonsupervised Learning Using Bayesian Estimation Technique
- X. Mode Estimation in Pattern Recognition
- XI. Conclusions and Further Remarks
- References
- Chapter 3. Algorithms for Pattern Classification
- I. Introduction
- II. Nonoverlapping Classes with Reliable Samples
- III. Nonoverlapping Classes with Erroneously Classified Samples
- IV. Overlapping Classes
- V. Multiclass Algorithms
- VI. Comparison and Discussion of the Various Algorithms
- References
- Chapter 4. Applications of Pattern Recognition Technology
- I. Introduction
- II. Pattern Recognition Mechanisms
- III. Applications
- Appendix
- References
- Chapter 5. Synthesis of Quasi-Optimal Switching Surfaces by Means of Training Techniques
- I. Introduction
- II. Quasi-Optimal Control
- III. The Method of Trainable Controllers
- IV. Feature Processing
- V. Applications: A Brief Review
- VI. Conclusions
- References
- Part I Problems
- PART II: ADAPTIVE AND LEARNING SYSTEMS
- Chapter 6. Gradient Identification for Linear Systems
- I. Introduction
- II. System Description
- III. Gradient Identification Algorithms: Stationary Parameters
- IV. Gradient Identification Algorithms: Time-Varying Para-meters
- V. Noisy Measurement Situation
- VI. Conclusions
- References
- Chapter 7. Adaptive Optimization Procedures
- I. Introduction
- II. Unimodal Techniques
- III. Multimodal Techniques
- IV. Conclusions
- References
- Chapter 8. Reinforcement-Learning Control and Pattern Recognition Systems
- I. Introduction
- II. Formulation of a Stochastic, Reinforcement-Learning Model
- III. Reinforcement-Learning Control Systems
- IV. Reinforcement-Learning Pattern Recognition Systems
- References
- Part II: Problems
- PART III: SPECIAL TOPICS
- Chapter 9. Stochastic Approximation
- I. Introduction
- II. Algorithms for Finding Zeroes of Functions
- III. Kiefer-Wolfowitz Schemes
- IV. Recovery of Functions from Noisy Measurements
- V. Convergence Rates
- VI. Methods of Accelerating Convergence
- VII. Conclusion
- Appendix 1
- Appendix 2
- References
- Chapter 10. Applications of the Stochastic Approximation Methods
- I. Introduction
- II. Pattern Classification Examples
- III. Estimation of Probability Distribution and Density Functions
- IV. State and Parameter Estimation Methods
- V. Bang-Bang Feedback Control
- VI. Conclusions
- References
- Chapter 11. Stochastic Automata As Models of Learning Systems
- I. Introduction to Stochastic Automata
- II. Synthesis of Stochastic Automata
- III. Deterministic Automata Operating in Random Environments
- IV. Variable Structure Stochastic Automata As Models of Learning Systems
- V. Generalizations of the Basic Reinforcement Learning Model
- VI. Automata Games
- VII. Conclusions and Further Remarks
- VIII. Nomenclature
- References
- Part III: Problems
- Index
