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
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_318607 |
| Date | January 1990 |
| Contributors | Wong, Man Leung., Chinese University of Hong Kong Graduate School. Division of Computer Science. |
| Publisher | Chinese University of Hong Kong |
| Source Sets | The Chinese University of Hong Kong |
| Language | English |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, [6], 206 leaves ; 30 cm. |
| Rights | Use 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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