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Pattern recognition and machine learning /
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 necessar...
| Clasificación: | Libro Electrónico |
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| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés Japonés |
| Publicado: |
Boston :
Academic Press,
©1992.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- 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.