Sequential Methods in Pattern Recognition and Machine Learning
Published on 14. May 2014
245 pages
978-0-08-095559-9 (ISBN)
Description
Sequential Methods in Pattern Recognition and Machine Learning
More details
Series
Language
English
Place of publication
San Diego
United States
ISBN-13
978-0-08-095559-9 (9780080955599)
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Book
02/1969
Academic Press
€65.61
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Content
- Front Cover
- Sequential Methods in Pattern Recognition and Machine Learning
- Copyright Page
- Contents
- Preface
- Chapter 1. Introduction
- 1.1 Pattern Recognition
- 1.2 Deterministic Classification Techniques
- 1.3 Training in Linear Classifiers
- 1.4 Statistical Classification Techniques
- 1.5 Sequential Decision Model for Pattern Classification
- 1.6 Learning in Sequential Pattern Recognition Systems
- 1.7 Summary and Further Remarks
- References
- Chapter 2. Feature Selection and Feature Ordering
- 2.1 Feature Selection and Ordering-Information Theoretic Approach
- 2.2 Feature Selection and Ordering-Karhunen-Loève Expansion
- 2.3 Illustrative Examples
- 2.4 Summary and Further Remarks
- References
- Chapter 3. Forward Procedure for Finite Sequential Classification Using Modified Sequential Probability Ratio Test
- 3.1 Introduction
- 3.2 Modified Sequential Probability Ratio Test-Discrete Case
- 3.3 Modified Sequential Probability Ratio Test-Continuous Case
- 3.4 Procedure of Modified Generalized Sequential Probability Ratio Test
- 3.5 Experiments in Pattern Classification
- 3.6 Summary and Further Remarks
- References
- Chapter 4. Backward Procedure for Finite Sequential Recognition Using Dynamic Programming
- 4.1 Introduction
- 4.2 Mathematical Formulation and Basic Functional Equation
- 4.3 Reduction of Dimensionality
- 4.4 Experiments in Pattern Classification
- 4.5 Backward Procedure for Both Feature Ordering and Pattern Classification
- 4.6 Experiments in Feature Ordering and Pattern Classification
- 4.7 Use of Dynamic Programming for Feature-Subset Selection
- 4.8 Suboptimal Sequential Pattern Recognition
- 4.9 Summary and Further Remarks
- References
- Chapter 5. Nonparametric Procedure in Sequential Pattern Classification
- 5.1 Introduction
- 5.2 Sequential Ranks and Sequential Ranking Procedure
- 5.3 A Sequential Two-Sample Test Problem
- 5.4 Nonparametric Design of Sequential Pattern Classifiers
- 5.5 Analysis of Optimal Performance and a Multiclass Generalization
- 5.6 Experimental Results and Discussions
- 5.7 Summary and Further Remarks
- References
- Chapter 6. Bayesian Learning in Sequential Pattern Recognition Systems
- 6.1 Supervised Learning Using Bayesian Estimation Techniques
- 6.2 Nonsupervised Learning Using Bayesian Estimation Techniques
- 6.3 Bayesian Learning of Slowly Varying Patterns
- 6.4 Learning of Parameters Using an Empirical Bayes Approach
- 6.5 A General Model for Bayesian Learning Systems
- 6.6 Summary and Further Remarks
- References
- Chapter 7. Learning in Sequential Recognition Systems Using Stochastic Approximation
- 7.1 Supervised Learning Using Stochastic Approximation
- 7.2 Nonsupervised Learning Using Stochastic Approximation
- 7.3 A General Formulation of Nonsupervised Learning Systems Using Stochastic Approximation
- 7.4 Learning of Slowly Time-Varying Parameters Using Dynamic Stochastic Approximation
- 7.5 Summary and Further Remarks
- References
- Appendix A. Introduction to Sequential Analysis
- 1. Sequential Probability Ratio Test
- 2. Bayes' Sequential Decision Procedure
- References
- Appendix B. Optimal Properties of Generalized Karhunen-Loève Expansion
- 1. Derivation of Property (i)
- 2. Derivation of Property (ii)
- Appendix C. Properties of the Modified SPRT
- Appendix D. Enumeration of Some Combinations of the kjs and Derivation of Formula for the Reduction of Tables Required in the Computation of Risk Functions
- Appendix E. Computations Required for the Feature Ordering and Pattern Classification Experiments Using Dynamic Programming
- Appendix F. Stochastic Approximation: A Brief Survey
- 1. Robbins-Monro Procedure for Estimating the Zero of an Unknown Regression Function
- 2. Kiefer-Wolfowitz Procedure for Estimating the Extremum of an Unknown Regression Function
- 3. Dvoretzky's Generalized Procedure
- 4. Methods of Accelerating Convergence
- 5. Dynamic Stochastic Approximation
- References
- Appendix G. The Method of Potential Functions or Reproducing Kernels
- 1. The Estimation of a Function with Noise-Free Measurements
- 2. The Estimation of a Function with Noisy Measurements
- 3. Pattern Classification-Deterministic Case
- 4. Pattern Classification-Statistical Case
- References
- Author Index
- Subject Index
