Realization of Generalized RBF Network
Neural classifiers have been widely used in many application areas. This paper describes generalized neural classifier based on the radial basis function network. The contributions of this work are: i) improvement on the standard radial basis function network architecture, ii) proposed a new cost fu...
| Main Authors: | , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
Malaysia: Universiti Malaysia Sarawak
2003
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/11952/ http://ir.unimas.my/id/eprint/11952/1/Realization%20of%20Generalized%20RBF%20Network_abstract.pdf |
| _version_ | 1848837095548780544 |
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| author | Lee, Nung Kion Wang, Dianhui |
| author_facet | Lee, Nung Kion Wang, Dianhui |
| author_sort | Lee, Nung Kion |
| building | UNIMAS Institutional Repository |
| collection | Online Access |
| description | Neural classifiers have been widely used in many application areas. This paper describes generalized neural classifier based on the radial basis function network. The contributions of this work are: i) improvement on the standard radial basis function network architecture, ii) proposed a new cost function for classification,
iii) hidden units feature subset selection algorithm, and iv) optimizing the neural classifier using the genetic algorithm with a new cost function. Comparative studies on the proposed neural classifier on protein classification problem are given. |
| first_indexed | 2025-11-15T06:34:13Z |
| format | Proceeding |
| id | unimas-11952 |
| institution | Universiti Malaysia Sarawak |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T06:34:13Z |
| publishDate | 2003 |
| publisher | Malaysia: Universiti Malaysia Sarawak |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | unimas-119522016-05-12T04:44:36Z http://ir.unimas.my/id/eprint/11952/ Realization of Generalized RBF Network Lee, Nung Kion Wang, Dianhui QA Mathematics T Technology (General) Neural classifiers have been widely used in many application areas. This paper describes generalized neural classifier based on the radial basis function network. The contributions of this work are: i) improvement on the standard radial basis function network architecture, ii) proposed a new cost function for classification, iii) hidden units feature subset selection algorithm, and iv) optimizing the neural classifier using the genetic algorithm with a new cost function. Comparative studies on the proposed neural classifier on protein classification problem are given. Malaysia: Universiti Malaysia Sarawak 2003 Proceeding NonPeerReviewed text en http://ir.unimas.my/id/eprint/11952/1/Realization%20of%20Generalized%20RBF%20Network_abstract.pdf Lee, Nung Kion and Wang, Dianhui (2003) Realization of Generalized RBF Network. In: International Conference of IT in Asia (CITA'03), July 2003. DOI: 10.13140/RG.2.1.5015.5281 |
| spellingShingle | QA Mathematics T Technology (General) Lee, Nung Kion Wang, Dianhui Realization of Generalized RBF Network |
| title | Realization of Generalized RBF Network |
| title_full | Realization of Generalized RBF Network |
| title_fullStr | Realization of Generalized RBF Network |
| title_full_unstemmed | Realization of Generalized RBF Network |
| title_short | Realization of Generalized RBF Network |
| title_sort | realization of generalized rbf network |
| topic | QA Mathematics T Technology (General) |
| url | http://ir.unimas.my/id/eprint/11952/ http://ir.unimas.my/id/eprint/11952/ http://ir.unimas.my/id/eprint/11952/1/Realization%20of%20Generalized%20RBF%20Network_abstract.pdf |