Multi-class contour preserving classification
Multi-class contour preserving classification
au.link.externalLink | [Full Text] (https://link.springer.com/chapter/10.1007%2F978-3-642-32639-4_5) | |
dc.contributor.author | Piyabute Fuangkhon | |
dc.contributor.author | Thitipong Tanprasert | |
dc.date.accessioned | 2018-04-17T07:36:39Z | |
dc.date.available | 2018-04-17T07:36:39Z | |
dc.date.issued | 2012-08 | |
dc.description.abstract | The original contour preserving classification technique was proposed to improve the robustness and weight fault tolerance of a neu- ral network applied with a two-class linearly separable problem. It was recently found to be improving the level of accuracy of two-class classi- fication. This paper presents an augmentation of the original technique to improve the level of accuracy of multi-class classification by better preservation of the shape or distribution model of a multi-class problem. The test results on six real world multi-class datasets from UCI ma- chine learning repository present that the proposed technique supports multi-class data and can improve the level of accuracy of multi-class classification more effectively. | |
dc.format.extent | 8 pages | en_US |
dc.format.mimetype | application/pdf | en_US |
dc.identifier.citation | Proceedings of the International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2012), Natal, Brazil, August 29-31, 2012:35-42 | en_US |
dc.identifier.uri | https://repository.au.edu/handle/6623004553/20661 | |
dc.language.iso | eng | en_US |
dc.rights.holder | Piyabuth Fuangkhon | en_US |
dc.rights.holder | Thitipong Tanprasert | en_US |
dc.subject | Contour preserving classification | |
dc.subject | Data preprocessor | |
dc.subject | Neural networks (Computer science) | |
dc.subject | Outpost vector | |
dc.subject | Pattern classification | |
dc.title | Multi-class contour preserving classification | en_US |
dc.type | Text | en_US |
mods.genre | Proceeding Paper | en_US |
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