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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| Format: | Thesis |
| Language: | English |
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2010
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| Online Access: | http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf |
| _version_ | 1797010911987236864 |
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| author | Yap , Keem Siah |
| author_facet | Yap , Keem Siah |
| author_sort | Yap , Keem Siah |
| collection | USM |
| 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. |
| first_indexed | 2024-03-06T15:26:35Z |
| format | Thesis |
| id | usm.eprints-42853 |
| institution | Universiti Sains Malaysia |
| language | English |
| last_indexed | 2024-03-06T15:26:35Z |
| publishDate | 2010 |
| record_format | dspace |
| spelling | usm.eprints-428532019-04-12T05:26:53Z 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. |
| 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 |
| title | Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
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| title_full | Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
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| title_fullStr | Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
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| title_full_unstemmed | Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
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| title_short | Novel Art-Based Neural Network Models For Pattern Classification, Rule Extraction And Data Regression
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| title_sort | novel art based neural network models for pattern classification rule extraction and data regression |
| topic | TK1-9971 Electrical engineering. Electronics. Nuclear engineering |
| url | http://eprints.usm.my/42853/1/YAP_KEEM_SIAH.pdf |
| work_keys_str_mv | AT yapkeemsiah novelartbasedneuralnetworkmodelsforpatternclassificationruleextractionanddataregression |