Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression

This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to co...

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Main Author: Yap , Keem Siah
Format: Thesis
Language:English
Published: 2010
Subjects:
Online Access:http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf
http://eprints.usm.my/42853/
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Institution: Universiti Sains Malaysia
Language: English
id my.usm.eprints.42853
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spelling my.usm.eprints.42853 http://eprints.usm.my/42853/ Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression Yap , Keem Siah TK1-9971 Electrical engineering. Electronics. Nuclear engineering This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to continuous learning of new knowledge without disturbing the existing knowledge base and without re-iterating through the training samples. The Adaptive Resonance Theory (ART) and Generalized Regression Neural Network (GRNN) models are employed as the backbone in this research. 2010-05 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf Yap , Keem Siah (2010) Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression. PhD thesis, Universiti Sains Malaysia.
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic TK1-9971 Electrical engineering. Electronics. Nuclear engineering
spellingShingle TK1-9971 Electrical engineering. Electronics. Nuclear engineering
Yap , Keem Siah
Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
description This thesis is concerned with the development of novel neural network models for tackling pattern classification, rule extraction, and data regression problems. The research focuses on one of the advanced features of neural networks, i.e., the incremental learning ability. This ability relates to continuous learning of new knowledge without disturbing the existing knowledge base and without re-iterating through the training samples. The Adaptive Resonance Theory (ART) and Generalized Regression Neural Network (GRNN) models are employed as the backbone in this research.
format Thesis
author Yap , Keem Siah
author_facet Yap , Keem Siah
author_sort Yap , Keem Siah
title Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_short Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_full Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_fullStr Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_full_unstemmed Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
title_sort novel art-based neural network models for pattern classification, rule extraction and data regression
publishDate 2010
url http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf
http://eprints.usm.my/42853/
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