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by Y. Anzai
Pattern Recognition and Machine Learning
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Title page
Table of Contents
Copyright
Preface
Study Guide
Chapter 1: Recognition and Learning by a Computer
1.1 What Is Recognition by a Computer?
1.2 Representation and Transformation in Recognition
1.3 What Is Learning by a Computer?
1.4 Representation and Transformation in Learning
1.5 Example of Recognition/Learning System
Summary
Exercises
Chapter 2: Representing Information
2.1 Pattern Function and Bit Pattern
2.2 The Representation of Spatial Structure
2.3 Graph Representation
2.4 Tree Representation
2.5 List Representation
2.6 Predicate Logic Representation
2.7 Horn Clause Logic Representation
2.8 Declarative Representation
2.9 Procedural Representation
2.10 Representation Using Rules
2.11 Semantic Networks and Frames
2.12 Representation Using Fourier Series
2.13 Classification of Representation Methods
Summary
Exercises
Chapter 3: Generation and Transformation of Representations
3.1 Methods of Generating and Transforming Representations
3.2 Linear Transformations of Pattern Functions
3.3 Sampling and Quantization of Pattern Functions
3.4 Transformation to Spatial Representations
3.5 Generation of Tree Representation
3.6 Search and Problem Solving
3.7 Logical Inference
3.8 Production Systems
3.9 Inference Using Frames
3.10 Constraint Representation and Relaxation
3.11 Summary
Exercises
Chapter 4: Pattern Feature Extraction
4.1 Detecting an Edge
4.2 Detection of a Boundary Line
4.3 Extracting a Region
4.4 Texture Analysis
4.5 Detection of Movement
4.6 Representing a Boundary Line
4.7 Representing a Region
4.8 Representation of a Solid
4.9 Interpretation of Line Drawings
Summary
Exercises
Chapter 5: Pattern Understanding Methods
5.1 Pattern Understanding and Knowledge Representation
5.2 Pattern Matching and the Relaxation Method
5.3 Maximal Subgraph Isomorphism and Clique Method
5.4 Control in Pattern Understanding
Summary
Exercises
Chapter 6: Learning Concepts
6.1 Definition of a Concept
6.2 Methods for Concept Learning
6.3 Generalization of Well-Formed Formulas
6.4 Version Space
6.5 Conceptual Clustering
Summary
Exercises
Chapter 7: Learning Procedures
7.1 Learning Operators in Problem Solving
7.2 Learning Rules
7.3 Learning Programs
Summary
Exercises
Chapter 8: Learning Based on Logic
8.1 Explanation-Based Learning
8.2 Analogical Learning
8.3 Nonmonotonic Logic and Learning
Summary
Keywords
Exercises
Chapter 9: Learning by Classification and Discovery
9.1 Representing Instances by a Decision Tree
9.2 An Algorithm for Generating a Decision Tree
9.3 Selecting a Test in Generating a Decision Tree
9.4 Learning from Noisy Data
9.5 Learning by Discovery
9.6 Discovery of New Concepts and Rules
Summary
Exercises
Chapter 10: Learning by Neural Networks
10.1 Representing neural networks
10.2 Back Propagation
10.3 Competitive Learning
10.4 Hopfield Networks
10.5 Boltzmann Machines
10.6 Parallel Computation in Recognition and Learning
Summary
Exercises
Appendix: Examples of Learning by Neural Networks
Answers
Bibliography
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