UID:
almafu_9958100984902883
Format:
1 online resource (461 p.)
ISBN:
1-282-28861-X
,
9786612288616
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0-08-095575-4
Series Statement:
Mathematics in science and engineering ; v. 66
Content:
Adaptive, learning, and pattern recognition systems; theory and applications
Note:
Description based upon print version of record.
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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
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IV. Backward Sequential Classification Procedure Using Dynamic ProgrammingV. 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
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II. Nonoverlapping Classes with Reliable SamplesIII. 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
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V. Applications: A Brief ReviewVI. 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
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Chapter 8. Reinforcement-Learning Control and Pattern Recognition SystemsI. 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
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References
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English
Additional Edition:
ISBN 0-12-490750-4
Language:
English
