Foundations of Soft Case-Based Reasoning
(Sprache: Englisch)
Case-based reasoning has been successfully implemented in a range of fuzzy logic and neural network systems. These systems are used in many important engineering and business applications where decisions must be made on the basis of uncertain or incomplete...
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Case-based reasoning has been successfully implemented in a range of fuzzy logic and neural network systems. These systems are used in many important engineering and business applications where decisions must be made on the basis of uncertain or incomplete information. The best examples are in system control, data mining, pattern recognition, and computer vision, but the list is growing constantly. This book provides a detailed description of the basic principles and applications of case-based reasoning in soft computing systems.
Case-based reasoning has been successfully implemented in a range of fuzzy logic and neural network systems. These systems are used in many important engineering and business applications where decisions must be made on the basis of uncertain or incomplete information. The best examples are in system control, data mining, pattern recognition, and computer vision, but the list is growing constantly. This book provides a detailed description of the basic principles and applications of case-based reasoning in soft computing systems.
Inhaltsverzeichnis zu „Foundations of Soft Case-Based Reasoning “
FOREWORD.PREFACE.
ABOUT THE AUTHORS.
1 INTRODUCTION.
1.1 Background.
1.2 Components and Features of Case-Based Reasoning.
1.3 Guidelines for the Use of Case-Based Reasoning.
1.4 Advantages of Using Case-Based Reasoning.
1.5 Case Representation and Indexing.
1.6 Case Retrieval.
1.7 Case Adaptation.
1.8 Case Learning and Case-Base Maintenance.
1.9 Example of Building a Case-Based Reasoning System.
1.10 Case-Based Reasoning: Methodology or Technology?
1.11 Soft Case-Based Reasoning.
1.12 Summary.
References.
2 CASE REPRESENTATION AND INDEXING.
2.1 Introduction.
2.2 Traditional Methods of Case Representation.
2.3 Soft Computing Techniques for Case Representation.
2.4 Case Indexing.
2.5 Summary.
References.
3 CASE SELECTION AND RETRIEVAL.
3.1 Introduction.
3.2 Similarity Concept.
3.3 Concept of Fuzzy Sets in Measuring Similarity.
3.4 Fuzzy Classification and Clustering of Cases.
3.5 Case Feature Weighting.
3.6 Case Selection and Retrieval Using Neural Networks.
3.7 Case Selection Using a Neuro-Fuzzy Model.
3.8 Case Selection Using Rough-Self Organizing Map.
3.9 Summary.
References.
4 CASE ADAPTATION.
4.1 Introduction.
4.2 Traditional Case Adaptation Strategies.
4.3 Some Case Adaptation Methods.
4.4 Case Adaptation Through Machine Learning.
4.5 Summary.
References.
5 CASE-BASE MAINTENANCE.
5.1 Introduction.
5.2 Background.
5.3 Types of Case-Base Maintenance.
5.4 Case-Base Maintenance Using a Rough-Fuzzy Approach.
5.5 Case-Base Maintenance Using a Fuzzy Integral Approach.
5.6 Summary.
References.
6 APPLICATIONS.
6.1 Introduction.
6.2 Web Mining.
6.3 Medical Diagnosis.
6.4 Weather Prediction.
6.5 Legal Inference.
6.6 Property Valuation.6.7 Corporate Bond Rating.
6.8 Color Matching.
6.9 Shoe Design.
6.10 Other Applications.
6.11 Summary.
References.
APPENDIXES.
A FUZZY LOGIC.
A.1 Fuzzy Subsets.
A.2 Membership Functions.
A.3 Operations on Fuzzy Subsets.
A.4 Measure of Fuzziness.
A.5 Fuzzy
... mehr
Rules.
References.
B ARTIFICIAL NEURAL NETWORKS.
B.1 Architecture of Artificial Neural Networks.
B.2 Training of Artificial Neural Networks.
B.3 ANN Models.
References.
C GENETIC ALGORITHMS.
C.1 Basic Principles.
C.2 Standard Genetic Algorithm.
C.3 Examples.
References.
D ROUGH SETS.
D.1 Information Systems.
D.2 Indiscernibility Relation.
D.3 Set Approximations.
D.4 Rough Membership.
D.5 Dependency of Attributes.
References.
INDEX.
References.
B ARTIFICIAL NEURAL NETWORKS.
B.1 Architecture of Artificial Neural Networks.
B.2 Training of Artificial Neural Networks.
B.3 ANN Models.
References.
C GENETIC ALGORITHMS.
C.1 Basic Principles.
C.2 Standard Genetic Algorithm.
C.3 Examples.
References.
D ROUGH SETS.
D.1 Information Systems.
D.2 Indiscernibility Relation.
D.3 Set Approximations.
D.4 Rough Membership.
D.5 Dependency of Attributes.
References.
INDEX.
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Autoren-Porträt von Simon Shiu, Sankar K. Pal, Tharam S. Dillon
Simon C. K. Shiu, PhD, is Assistant Professor in the Department of Computing at Hong Kong Polytechnic University.
Bibliographische Angaben
- Autoren: Simon Shiu , Sankar K. Pal , Tharam S. Dillon
- 2004, 300 Seiten, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0471086355
- ISBN-13: 9780471086352
Sprache:
Englisch
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