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Pattern Recognition & Machine Learning

Book Description

This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.

Table of Contents

  1. Cover image
  2. Title page
  3. Table of Contents
  4. Copyright
  5. Preface
  6. Study Guide
  7. Chapter 1: Recognition and Learning by a Computer
    1. 1.1 What Is Recognition by a Computer?
    2. 1.2 Representation and Transformation in Recognition
    3. 1.3 What Is Learning by a Computer?
    4. 1.4 Representation and Transformation in Learning
    5. 1.5 Example of Recognition/Learning System
    6. Summary
    7. Exercises
  8. Chapter 2: Representing Information
    1. 2.1 Pattern Function and Bit Pattern
    2. 2.2 The Representation of Spatial Structure
    3. 2.3 Graph Representation
    4. 2.4 Tree Representation
    5. 2.5 List Representation
    6. 2.6 Predicate Logic Representation
    7. 2.7 Horn Clause Logic Representation
    8. 2.8 Declarative Representation
    9. 2.9 Procedural Representation
    10. 2.10 Representation Using Rules
    11. 2.11 Semantic Networks and Frames
    12. 2.12 Representation Using Fourier Series
    13. 2.13 Classification of Representation Methods
    14. Summary
    15. Exercises
  9. Chapter 3: Generation and Transformation of Representations
    1. 3.1 Methods of Generating and Transforming Representations
    2. 3.2 Linear Transformations of Pattern Functions
    3. 3.3 Sampling and Quantization of Pattern Functions
    4. 3.4 Transformation to Spatial Representations
    5. 3.5 Generation of Tree Representation
    6. 3.6 Search and Problem Solving
    7. 3.7 Logical Inference
    8. 3.8 Production Systems
    9. 3.9 Inference Using Frames
    10. 3.10 Constraint Representation and Relaxation
    11. 3.11 Summary
    12. Exercises
  10. Chapter 4: Pattern Feature Extraction
    1. 4.1 Detecting an Edge
    2. 4.2 Detection of a Boundary Line
    3. 4.3 Extracting a Region
    4. 4.4 Texture Analysis
    5. 4.5 Detection of Movement
    6. 4.6 Representing a Boundary Line
    7. 4.7 Representing a Region
    8. 4.8 Representation of a Solid
    9. 4.9 Interpretation of Line Drawings
    10. Summary
    11. Exercises
  11. Chapter 5: Pattern Understanding Methods
    1. 5.1 Pattern Understanding and Knowledge Representation
    2. 5.2 Pattern Matching and the Relaxation Method
    3. 5.3 Maximal Subgraph Isomorphism and Clique Method
    4. 5.4 Control in Pattern Understanding
    5. Summary
    6. Exercises
  12. Chapter 6: Learning Concepts
    1. 6.1 Definition of a Concept
    2. 6.2 Methods for Concept Learning
    3. 6.3 Generalization of Well-Formed Formulas
    4. 6.4 Version Space
    5. 6.5 Conceptual Clustering
    6. Summary
    7. Exercises
  13. Chapter 7: Learning Procedures
    1. 7.1 Learning Operators in Problem Solving
    2. 7.2 Learning Rules
    3. 7.3 Learning Programs
    4. Summary
    5. Exercises
  14. Chapter 8: Learning Based on Logic
    1. 8.1 Explanation-Based Learning
    2. 8.2 Analogical Learning
    3. 8.3 Nonmonotonic Logic and Learning
    4. Summary
    5. Keywords
    6. Exercises
  15. Chapter 9: Learning by Classification and Discovery
    1. 9.1 Representing Instances by a Decision Tree
    2. 9.2 An Algorithm for Generating a Decision Tree
    3. 9.3 Selecting a Test in Generating a Decision Tree
    4. 9.4 Learning from Noisy Data
    5. 9.5 Learning by Discovery
    6. 9.6 Discovery of New Concepts and Rules
    7. Summary
    8. Exercises
  16. Chapter 10: Learning by Neural Networks
    1. 10.1 Representing neural networks
    2. 10.2 Back Propagation
    3. 10.3 Competitive Learning
    4. 10.4 Hopfield Networks
    5. 10.5 Boltzmann Machines
    6. 10.6 Parallel Computation in Recognition and Learning
    7. Summary
    8. Exercises
  17. Appendix: Examples of Learning by Neural Networks
  18. Answers
  19. Bibliography
  20. Index