Computer-Oriented Approaches to Pattern Recognition

Name: Computer-Oriented Approaches to Pattern Recognition
Brand: Academic Press
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Meisel(Autor*in)

Meisel(Herausgeber*in)

Academic Press

Erschienen am 14. Mai 2014

263 Seiten

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Inhalt

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
Selected Bibliography
CHAPTER 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
4.5 Minimizing the Approximate Risk
4.6 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
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
7.4 Indirect Approaches to Deriving Piecewise Linear Discriminant Functions
7.5 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
8.9 Unsupervised and Decision-Directed Learning
8.10 Clustering as Data Analysis
8.11 Cluster Analysis and Pattern Classification
Exercises
Selected Bibliography
CHAPTER IX. FEATURE SELECTION
9.1 Introduction
9.2 Direct Methods
9.3 Indirect Methods: Parameterized Transformations
9.4 Measures of Quality: Tnterset and Intraset Distances
9.5 Measures of Quality Utilizing Probability Estimates
9.6 Measures of Quality: The Preservation of Structure
9.7 Indirect Methods: Other Measures of Quality
9.8 Custom Orthonormal Transformations
9.9 Choosing n of m Features
9.10 Conclusion
Exercises
Selected Bibliography
CHAPTER X. SPECIAL TOPICS
10.1 Introduction
10.2 Binary Variables
10.3 Sequential Feature Selection
10.4 Structural, Linguistic, and Heuristic Analysis of Patterns
10.5 Asymptotic Convergence and Stochastic Approximation
10.6 Nonstationary Pattern Recognition
Exercises
Selectcd Bibliography
Appendix A. A SET OF ORTHONORMAL POLYNOMIALS
Appendix B. EFFICIENT REPRESENTATION AND APPROXIMATION OF MULTIVARIATE FUNCTIONS
B.1 Introduction
B.2 Continuous Piecewise Linear Form Approximations
B.3 Composed Functions
Selected Bibliography
Index

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