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Learning of rule-based knowledge from inexact examples.

by Wong Man Leung. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1990. / Bibliography: leaves 200-206. / ABSTRACT --- p.A-1 / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Overview --- p.1 / Chapter 1.2 --- Properties of an ideal AKARS --- p.4 / Chapter 1.3 --- The architecture of AKARS-1 --- p.9 / Chapter 1.4 --- Research approach --- p.13 / Chapter 2 --- state of the art of machine learning --- p.16 / Chapter 2.1 --- Learning by instruction --- p.16 / Chapter 2.2 --- Learning by analogy --- p.17 / Chapter 2.3 --- Learning from examples --- p.17 / Chapter 2.4 --- Learning from observation and discovery --- p.24 / Chapter 3 --- SESS: a Simple Expert System Shell --- p.26 / Chapter 3.1 --- Overview of SESS --- p.26 / Chapter 3.2 --- Knowledge representation --- p.27 / Chapter 3.2.1 --- Representation of attribute --- p.27 / Chapter 3.2.2 --- Representation of fuzzy concepts --- p.28 / Chapter 3.2.3 --- Representation of rules --- p.29 / Chapter 3.3 --- Reasoning in SESS --- p.31 / Chapter 3.3.1 --- Rule evaluation --- p.31 / Chapter 3.3.2 --- Rules with multiple antecedent conditions --- p.32 / Chapter 3.3.3 --- Calculation of certainty factor --- p.33 / Chapter 4 --- A prototypical learning component --- p.35 / Chapter 4.1 --- A prototypical Automatic Knowledge Acquisition System: AKA-1 --- p.35 / Chapter 4.1.1 --- Introduction to AKA-1 --- p.35 / Chapter 4.1.2 --- A generic rule learning algorithm --- p.37 / Chapter 4.1.3 --- Method for evaluating the discriminatory abilities --- p.43 / Chapter 4.1.4 --- Method for determining the best attribute/value pair --- p.44 / Chapter 4.1.4.1 --- Determining the best nominal attribute/value pair --- p.44 / Chapter 4.1.4.2 --- Determining the best structural attribute/value pair --- p.46 / Chapter 4.1.4.3 --- Determining the best linear attribute/value pair --- p.48 / Chapter 4.1.5 --- Method for calculating certainty factors of rules --- p.50 / Chapter 4.1.6 --- Rule inducing algorithm of AKA-1 --- p.50 / Chapter 4.2 --- Generalizing production rules --- p.51 / Chapter 4.2.1 --- Testing significance of condition --- p.52 / Chapter 4.2.1.1 --- Chi-squre test (Large sample test) --- p.53 / Chapter 4.2.1.2 --- Fisher-Irwin's exact test (Small sample test) --- p.54 / Chapter 4.2.2 --- The generalization algorithm --- p.55 / Chapter 4.3 --- Case studies --- p.55 / Chapter 4.3.1 --- Case one --- p.56 / Chapter 4.3.2 --- Case two --- p.59 / Chapter 4.3.3 --- Case three --- p.59 / Chapter 4.3.4 --- Comparison with ID3 --- p.60 / Chapter 5 --- inducing fuzzy rules from inexact examples --- p.62 / Chapter 5.1 --- Introduction to AKA-2 --- p.62 / Chapter 5.2 --- Notations --- p.63 / Chapter 5.3 --- Method for selecting attribute/value pairs in AKA-2 --- p.67 / Chapter 5.4 --- Evaluating certainty factors of rules --- p.72 / Chapter 6 --- HERES: a HEuristic REfinement System --- p.78 / Chapter 6.1 --- Introduction to HERES --- p.78 / Chapter 6.2 --- Refinement concepts of HERES --- p.79 / Chapter 6.2.1 --- Refinement operations --- p.80 / Chapter 6.2.2 --- Refinement phases of HERES --- p.81 / Chapter 6.2.3 --- Strategy for knowledge base refinement --- p.83 / Chapter 6.2.4 --- Refinement examples --- p.86 / Chapter 6.2.5 --- Performance statistics --- p.91 / Chapter 6.2.6 --- Rule statistics --- p.94 / Chapter 6.2.7 --- Summary of refinement concepts --- p.96 / Chapter 6.3 --- Logical structure of HERES --- p.98 / Chapter 6.4 --- Rule analysis --- p.101 / Chapter 6.4.1 --- Rule analysis for generalization --- p.101 / Chapter 6.4.1.1 --- For rules with non-fuzzy final conclusions --- p.101 / Chapter 6.4.1.2 --- For hierarchical rules with non-fuzzy final conclusion --- p.107 / Chapter 6.4.1.3 --- For rules with fuzzy final conclusions --- p.108 / Chapter 6.4.2 --- Rule analysis for specialization --- p.108 / Chapter 6.4.2.1 --- For rules with non-fuzzy final conclusions --- p.108 / Chapter 6.4.2.2 --- For rules with fuzzy conclusions --- p.111 / Chapter 6.5 --- Modification of rule statistics --- p.111 / Chapter 6.6 --- First order G-3 refinement --- p.117 / Chapter 6.7 --- Higher order G-S refinement --- p.120 / Chapter 6.8 --- Heuristics of HERES --- p.122 / Chapter 6.8.1 --- Notations --- p.123 / Chapter 6.8.2 --- Control heuristics --- p.124 / Chapter 6.8.3 --- Strategic heuristics --- p.126 / Chapter 6.8.3.1 --- Strategic heuristics for generalization --- p.127 / Chapter 6.8.3.2 --- Strategic heuristics for specialization --- p.127 / Chapter 6.8.4 --- Refinement heuristics --- p.128 / Chapter 6.8.4.1 --- Refinement heuristics for generalization --- p.128 / Chapter 6.8.4.2 --- Refinement heuristics for specialization --- p.131 / Chapter 6.9 --- Discussion --- p.132 / Chapter 7 --- Verification of AKARS-l --- p.134 / Chapter 7.1 --- Verification methodologies --- p.134 / Chapter 7.1.1 --- Existing examples methodology --- p.134 / Chapter 7.1.2 --- Existing knowledge base methodology --- p.135 / Chapter 7.2 --- Methods for evaluating the performance of a knowledge base --- p.141 / Chapter 7.2.1 --- First method (Successful rate method) --- p.141 / Chapter 7.2.2 --- Second method (MDCF method) --- p.145 / Chapter 7.3 --- Case studies --- p.149 / Chapter 7.3.1 --- Case one --- p.149 / Chapter 7.3.2 --- Case two --- p.152 / Chapter 7.3.3 --- Case three --- p.156 / Chapter 7.4 --- Verification results --- p.157 / Chapter 7.4.1 --- Results of case one --- p.157 / Chapter 7.4.1.1 --- First experiment --- p.157 / Chapter 7.4.1.2 --- Second experiment --- p.158 / Chapter 7.4.1.3 --- Discussion --- p.160 / Chapter 7.4.2 --- Results of case two --- p.162 / Chapter 7.4.2.1 --- First experiment --- p.162 / Chapter 7.4.2.2 --- Second experiment --- p.163 / Chapter 7.4.2.3 --- Third experiment --- p.165 / Chapter 7.4.2.4 --- Fourth experiment --- p.166 / Chapter 7.4.2.5 --- Discussion --- p.167 / Chapter 7.4.3 --- Results of case three --- p.169 / Chapter 7.5 --- Discussion --- p.172 / Chapter 8 --- Developing hierarchical knowledge bases --- p.173 / Chapter 8.1 --- Introduction --- p.173 / Chapter 8.2 --- Acquire hierarchical knowledge bases by using AKARS-1 --- p.176 / Chapter 8.2.1 --- Difficulties of inducing multiple-level rules --- p.176 / Chapter 8.2.2 --- Structural approach of building hierarchical knowledge bases --- p.179 / Chapter 8.3 --- Further improvement on AKARS-1 to induce hierarchical knowledge bases --- p.181 / Chapter 9 --- Conclusion --- p.182 / Appendix A --- p.186 / Appendix B --- p.187 / Appendix C --- p.188 / Appendix D --- p.193 / Appendix E --- p.194 / Reference --- p.200

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_318607
Date January 1990
ContributorsWong, Man Leung., Chinese University of Hong Kong Graduate School. Division of Computer Science.
PublisherChinese University of Hong Kong
Source SetsThe Chinese University of Hong Kong
LanguageEnglish
Detected LanguageEnglish
TypeText, bibliography
Formatprint, [6], 206 leaves ; 30 cm.
RightsUse of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/)

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