Keung Chi-Kin. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves 128-135). / Abstracts in English and Chinese. / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Classification --- p.2 / Chapter 1.2 --- Instance-Based Learning --- p.4 / Chapter 1.2.1 --- Three Basic Components --- p.5 / Chapter 1.2.2 --- Advantages --- p.6 / Chapter 1.2.3 --- Disadvantages --- p.7 / Chapter 1.3 --- Thesis Contributions --- p.7 / Chapter 1.4 --- Thesis Organization --- p.8 / Chapter 2 --- Background --- p.10 / Chapter 2.1 --- Improving Instance-Based Learning --- p.10 / Chapter 2.1.1 --- Scaling-up Nearest Neighbor Searching --- p.11 / Chapter 2.1.2 --- Data Reduction --- p.12 / Chapter 2.2 --- Prototype Learning --- p.12 / Chapter 2.2.1 --- Objectives --- p.13 / Chapter 2.2.2 --- Two Types of Prototype Learning --- p.15 / Chapter 2.3 --- Instance-Filtering Methods --- p.15 / Chapter 2.3.1 --- Retaining Border Instances --- p.16 / Chapter 2.3.2 --- Removing Border Instances --- p.21 / Chapter 2.3.3 --- Retaining Center Instances --- p.22 / Chapter 2.3.4 --- Advantages --- p.23 / Chapter 2.3.5 --- Disadvantages --- p.24 / Chapter 2.4 --- Instance-Abstraction Methods --- p.25 / Chapter 2.4.1 --- Advantages --- p.30 / Chapter 2.4.2 --- Disadvantages --- p.30 / Chapter 2.5 --- Other Methods --- p.32 / Chapter 2.6 --- Summary --- p.34 / Chapter 3 --- Integration of Filtering and Abstraction --- p.36 / Chapter 3.1 --- Incremental Integration --- p.37 / Chapter 3.1.1 --- Motivation --- p.37 / Chapter 3.1.2 --- The Integration Method --- p.40 / Chapter 3.1.3 --- Issues --- p.41 / Chapter 3.2 --- Concept Integration --- p.42 / Chapter 3.2.1 --- Motivation --- p.43 / Chapter 3.2.2 --- The Integration Method --- p.44 / Chapter 3.2.3 --- Issues --- p.45 / Chapter 3.3 --- Difference between Integration Methods and Composite Clas- sifiers --- p.48 / Chapter 4 --- The PGF Framework --- p.49 / Chapter 4.1 --- The PGF1 Algorithm --- p.50 / Chapter 4.1.1 --- Instance-Filtering Component --- p.51 / Chapter 4.1.2 --- Instance-Abstraction Component --- p.52 / Chapter 4.2 --- The PGF2 Algorithm --- p.56 / Chapter 4.3 --- Empirical Analysis --- p.57 / Chapter 4.3.1 --- Experimental Setup --- p.57 / Chapter 4.3.2 --- Results of PGF Algorithms --- p.59 / Chapter 4.3.3 --- Analysis of PGF1 --- p.61 / Chapter 4.3.4 --- Analysis of PGF2 --- p.63 / Chapter 4.3.5 --- Overall Behavior of PGF --- p.66 / Chapter 4.3.6 --- Comparisons with Other Approaches --- p.69 / Chapter 4.4 --- Time Complexity --- p.72 / Chapter 4.4.1 --- Filtering Components --- p.72 / Chapter 4.4.2 --- Abstraction Component --- p.74 / Chapter 4.4.3 --- PGF Algorithms --- p.74 / Chapter 4.5 --- Summary --- p.75 / Chapter 5 --- Integrated Concept Prototype Learner --- p.77 / Chapter 5.1 --- Motivation --- p.78 / Chapter 5.2 --- Abstraction Component --- p.80 / Chapter 5.2.1 --- Issues for Abstraction --- p.80 / Chapter 5.2.2 --- Investigation on Typicality --- p.82 / Chapter 5.2.3 --- Typicality in Abstraction --- p.85 / Chapter 5.2.4 --- The TPA algorithm --- p.86 / Chapter 5.2.5 --- Analysis of TPA --- p.90 / Chapter 5.3 --- Filtering Component --- p.93 / Chapter 5.3.1 --- Investigation on Associate --- p.96 / Chapter 5.3.2 --- The RT2 Algorithm --- p.100 / Chapter 5.3.3 --- Analysis of RT2 --- p.101 / Chapter 5.4 --- Concept Integration --- p.103 / Chapter 5.4.1 --- The ICPL Algorithm --- p.104 / Chapter 5.4.2 --- Analysis of ICPL --- p.106 / Chapter 5.5 --- Empirical Analysis --- p.106 / Chapter 5.5.1 --- Experimental Setup --- p.106 / Chapter 5.5.2 --- Results of ICPL Algorithm --- p.109 / Chapter 5.5.3 --- Comparisons with Pure Abstraction and Pure Filtering --- p.110 / Chapter 5.5.4 --- Comparisons with Other Approaches --- p.114 / Chapter 5.6 --- Time Complexity --- p.119 / Chapter 5.7 --- Summary --- p.120 / Chapter 6 --- Conclusions and Future Work --- p.122 / Chapter 6.1 --- Conclusions --- p.122 / Chapter 6.2 --- Future Work --- p.126 / Bibliography --- p.128 / Chapter A --- Detailed Information for Tested Data Sets --- p.136 / Chapter B --- Detailed Experimental Results for PGF --- p.138
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_323239 |
| Date | January 2000 |
| Contributors | Keung, Chi-Kin., Chinese University of Hong Kong Graduate School. Division of Systems Engineering and Engineering Management. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xi, 141 leaves : ill. ; 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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