CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
thesis
Investigation on prototype learning.
Authors
Publication date
1 January 2000
Publisher
Abstract
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.1Chapter 1.1 --- Classification --- p.2Chapter 1.2 --- Instance-Based Learning --- p.4Chapter 1.2.1 --- Three Basic Components --- p.5Chapter 1.2.2 --- Advantages --- p.6Chapter 1.2.3 --- Disadvantages --- p.7Chapter 1.3 --- Thesis Contributions --- p.7Chapter 1.4 --- Thesis Organization --- p.8Chapter 2 --- Background --- p.10Chapter 2.1 --- Improving Instance-Based Learning --- p.10Chapter 2.1.1 --- Scaling-up Nearest Neighbor Searching --- p.11Chapter 2.1.2 --- Data Reduction --- p.12Chapter 2.2 --- Prototype Learning --- p.12Chapter 2.2.1 --- Objectives --- p.13Chapter 2.2.2 --- Two Types of Prototype Learning --- p.15Chapter 2.3 --- Instance-Filtering Methods --- p.15Chapter 2.3.1 --- Retaining Border Instances --- p.16Chapter 2.3.2 --- Removing Border Instances --- p.21Chapter 2.3.3 --- Retaining Center Instances --- p.22Chapter 2.3.4 --- Advantages --- p.23Chapter 2.3.5 --- Disadvantages --- p.24Chapter 2.4 --- Instance-Abstraction Methods --- p.25Chapter 2.4.1 --- Advantages --- p.30Chapter 2.4.2 --- Disadvantages --- p.30Chapter 2.5 --- Other Methods --- p.32Chapter 2.6 --- Summary --- p.34Chapter 3 --- Integration of Filtering and Abstraction --- p.36Chapter 3.1 --- Incremental Integration --- p.37Chapter 3.1.1 --- Motivation --- p.37Chapter 3.1.2 --- The Integration Method --- p.40Chapter 3.1.3 --- Issues --- p.41Chapter 3.2 --- Concept Integration --- p.42Chapter 3.2.1 --- Motivation --- p.43Chapter 3.2.2 --- The Integration Method --- p.44Chapter 3.2.3 --- Issues --- p.45Chapter 3.3 --- Difference between Integration Methods and Composite Clas- sifiers --- p.48Chapter 4 --- The PGF Framework --- p.49Chapter 4.1 --- The PGF1 Algorithm --- p.50Chapter 4.1.1 --- Instance-Filtering Component --- p.51Chapter 4.1.2 --- Instance-Abstraction Component --- p.52Chapter 4.2 --- The PGF2 Algorithm --- p.56Chapter 4.3 --- Empirical Analysis --- p.57Chapter 4.3.1 --- Experimental Setup --- p.57Chapter 4.3.2 --- Results of PGF Algorithms --- p.59Chapter 4.3.3 --- Analysis of PGF1 --- p.61Chapter 4.3.4 --- Analysis of PGF2 --- p.63Chapter 4.3.5 --- Overall Behavior of PGF --- p.66Chapter 4.3.6 --- Comparisons with Other Approaches --- p.69Chapter 4.4 --- Time Complexity --- p.72Chapter 4.4.1 --- Filtering Components --- p.72Chapter 4.4.2 --- Abstraction Component --- p.74Chapter 4.4.3 --- PGF Algorithms --- p.74Chapter 4.5 --- Summary --- p.75Chapter 5 --- Integrated Concept Prototype Learner --- p.77Chapter 5.1 --- Motivation --- p.78Chapter 5.2 --- Abstraction Component --- p.80Chapter 5.2.1 --- Issues for Abstraction --- p.80Chapter 5.2.2 --- Investigation on Typicality --- p.82Chapter 5.2.3 --- Typicality in Abstraction --- p.85Chapter 5.2.4 --- The TPA algorithm --- p.86Chapter 5.2.5 --- Analysis of TPA --- p.90Chapter 5.3 --- Filtering Component --- p.93Chapter 5.3.1 --- Investigation on Associate --- p.96Chapter 5.3.2 --- The RT2 Algorithm --- p.100Chapter 5.3.3 --- Analysis of RT2 --- p.101Chapter 5.4 --- Concept Integration --- p.103Chapter 5.4.1 --- The ICPL Algorithm --- p.104Chapter 5.4.2 --- Analysis of ICPL --- p.106Chapter 5.5 --- Empirical Analysis --- p.106Chapter 5.5.1 --- Experimental Setup --- p.106Chapter 5.5.2 --- Results of ICPL Algorithm --- p.109Chapter 5.5.3 --- Comparisons with Pure Abstraction and Pure Filtering --- p.110Chapter 5.5.4 --- Comparisons with Other Approaches --- p.114Chapter 5.6 --- Time Complexity --- p.119Chapter 5.7 --- Summary --- p.120Chapter 6 --- Conclusions and Future Work --- p.122Chapter 6.1 --- Conclusions --- p.122Chapter 6.2 --- Future Work --- p.126Bibliography --- p.128Chapter A --- Detailed Information for Tested Data Sets --- p.136Chapter B --- Detailed Experimental Results for PGF --- p.13
Similar works
Full text
Open in the Core reader
Download PDF
Available Versions
CUHK Digital Repository
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:cuhk-dr:cuhk_323239
Last time updated on 09/11/2016