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Pattern recognition and machine learning
1 online resource (xvi, 407 pages) : illustrations
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.
Front cover; Pattern Recognition and Machine Learning; Copyright page; Tabel of Contents; Preface; Study Guide; Chapter 1. Recognition and Learning by a Computer; 1.1 What Is Recognition by a Computer?; 1.2 Representation and Transformationin Recognition; 1.3 What Is Learning by a Computer?; 1.4 Representation and Transformationin Learning; 1.5 Example of Recognition/Learning System; Summary; Keywords; 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 Representation2.6 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; Keywords; 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 Representations3.5 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; Keywords; 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; Keywords.
ExercisesChapter 5. Pattern UnderstandingMethods; 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; Keywords; 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; Keywords; Exercises; Chapter 7. Learning Procedures; 7.1 Learning Operators in Problem Solving; 7.2 Learning Rules.
7.3 Learning ProgramsSummary; Keywords; 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; Keywords; Exercises; Chapter 10. Learning by Neural Networks.
Includes bibliographical references (pages 387-402) and index.
Reconnaissance des formes (informatique)