UID:
almahu_9947367119702882
Format:
1 online resource (263 p.)
ISBN:
1-282-28904-7
,
9786612289040
,
0-08-095592-4
Series Statement:
Mathematics in science and engineering ; 83
Content:
Computer-oriented approaches to pattern recognition
Note:
Description based upon print version of record.
,
Front Cover; Computer-Oriented Approaches to Pattern Recognition; Copyright Page; Contents; Preface; CHAPTER I. BASIC CONCEPTS AND METHODS IN MATHEMATICAL PATTERN RECOGNITION; 1.1 What Is Pattern Recognition?; 1.2 Computer-Oriented Approaches to Pattern Recognition; 1.3 Examples of Pattern Recognition Applications; 1.4 The Pattern Recognition Process; 1.5 Basic Concepts in Pattern Classification; 1.6 Basic Concepts in Feature Selection; 1.7 Direct and Indirect Methods; 1.8 Parametric and Nonparametric Methods; 1.9 Some Simple Pattern Classification Algorithms; 1.10 Overview; Exercises
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Selected BibliographyCHAPTER II. THE STATISTICAL FORMULATION AND PARAMETRIC METHODS; 2.1 Introduction; 2.2 Statistical Decision Theory; 2.3 Histograms; 2.4 Parametric Methods; 2.5 Conclusion; Exercises; Selected Bibliography; CHAPTER III. INTRODUCTION TO OPTIMIZATION TECHNIQUES; 3.1 Indirect Methods; 3.2 Direct Methods; 3.3 Linear Programming; 3.4 A Localized Random Search Technique; Exercises; Selected Bibliography; CHAPTER IV. LINEAR DISCRIMINANT FUNCTIONS AND EXTENSIONS; 4.1 Introduction; 4.2 Mathematical Preliminaries; 4.3 Linear Discriminant Functions; 4.4 Specifying the Loss Function
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4.5 Minimizing the Approximate Risk4.6 An Alternate Cost Function; 4.7 The N-Class Problem; 4.8 Solution by Linear Programming; 4.9 Extension to General F Functions; 4.10 Threshold Elements in the Realization of Linear Discriminant Functions; Exercises; Selected Bibliography; CHAPTER V. INDIRECT APPROXIMATION OF PROBABILITY DENSITIES; 5.1 Introduction; 5.2 Integral-Square Approximation; 5.3 Least-Mean-Square Approximation; 5.4 Weighted Mean-Square Approximation; 5.5 Relation to Linear Discriminant and F-Function Techniques; 5.6 Extensions; 5.7 Summary; Exercises; Selected Bibliography
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CHAPTER VI. DIRECT CONSTRUCTION OF PROBABILITY DENSITIES : POTENTIAL FUNCTIONS (PARZEN ESTIMATORS)6.1 Introduction; 6.2 An Alternate Interpretation; 6.3 Generality of Potential Function Methods; 6.4 Choosing the Form of Potential Function; 6.5 Choosing Size and Shape Parameters; 6.6 Efficient Decision Rules Based on Potential Function Methods; 6.7 Generalized Potential Functions; 6.8 Conclusion; Exercises; Selected Bibliography; CHAPTER VII. PIECEWISE LINEAR DISCRIMINANT FUNCTIONS; 7.1 Introduction; 7.2 Implicit Subclasses; 7.3 Perceptrons and Layered Networks
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7.4 Indirect Approaches to Deriving Piecewise Linear Discriminant Functions7.5 Piecewise Linear Decision Boundaries by Linear Programming; 7.6 Limiting the Class of Acceptable Boundaries; 7.7 Conclusion; Exercises; Selected Bibliography; CHAPTER VIll. CLUSTER ANALYSIS AND UNSUPERVISED LEARNING; 8.1 Introduction; 8.2 Describing the Subregions; 8.3 Cluster Analysis as Decomposition of Probability Densities; 8.4 Mode Seeking on Probability Estimates; 8.5 Iterative Adjustment of Clusters; 8.6 Adaptive Sample Set Construction; 8.7 Graph-Theoretic Methods; 8.8 Indirect Methods in Clustering
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8.9 Unsupervised and Decision-Directed Learning
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English
Additional Edition:
ISBN 0-12-488850-X
Language:
English
