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Investigation on prototype learning.

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

Identiferoai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_323239
Date January 2000
ContributorsKeung, Chi-Kin., Chinese University of Hong Kong Graduate School. Division of Systems Engineering and Engineering Management.
Source SetsThe Chinese University of Hong Kong
LanguageEnglish, Chinese
Detected LanguageEnglish
TypeText, bibliography
Formatprint, xi, 141 leaves : ill. ; 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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