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- Publisher Website: 10.1109/CSO.2010.70
- Scopus: eid_2-s2.0-77956442154
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Conference Paper: Support Vector Machine methods for the prediction of cancer growth
Title | Support Vector Machine methods for the prediction of cancer growth |
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Authors | |
Issue Date | 2010 |
Citation | 3Rd International Joint Conference On Computational Sciences And Optimization, Cso 2010: Theoretical Development And Engineering Practice, 2010, v. 1, p. 229-232 How to Cite? |
Abstract | In this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we propose a classifier which can differentiate patients having different levels of cancer growth with a high classification rate. To further improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection using rfe-gist, a special function of SVM. © 2010 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/158875 |
References |
DC Field | Value | Language |
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dc.contributor.author | Chen, X | en_US |
dc.contributor.author | Ching, WK | en_US |
dc.contributor.author | AokiKinoshita, KF | en_US |
dc.contributor.author | Furuta, K | en_US |
dc.date.accessioned | 2012-08-08T09:04:02Z | - |
dc.date.available | 2012-08-08T09:04:02Z | - |
dc.date.issued | 2010 | en_US |
dc.identifier.citation | 3Rd International Joint Conference On Computational Sciences And Optimization, Cso 2010: Theoretical Development And Engineering Practice, 2010, v. 1, p. 229-232 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/158875 | - |
dc.description.abstract | In this paper, we study the application of Support Vector Machine (SVM) in the prediction of cancer growth. SVM is known to be an efficient method and it has been widely used for classification problems. Here we propose a classifier which can differentiate patients having different levels of cancer growth with a high classification rate. To further improve the accuracy of classification, we propose to determine the optimal size of the training set and perform feature selection using rfe-gist, a special function of SVM. © 2010 IEEE. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | 3rd International Joint Conference on Computational Sciences and Optimization, CSO 2010: Theoretical Development and Engineering Practice | en_US |
dc.title | Support Vector Machine methods for the prediction of cancer growth | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Ching, WK:wching@hku.hk | en_US |
dc.identifier.authority | Ching, WK=rp00679 | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1109/CSO.2010.70 | en_US |
dc.identifier.scopus | eid_2-s2.0-77956442154 | en_US |
dc.identifier.hkuros | 170824 | - |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-77956442154&selection=ref&src=s&origin=recordpage | en_US |
dc.identifier.volume | 1 | en_US |
dc.identifier.spage | 229 | en_US |
dc.identifier.epage | 232 | en_US |
dc.identifier.scopusauthorid | Chen, X=35772404700 | en_US |
dc.identifier.scopusauthorid | Ching, WK=13310265500 | en_US |
dc.identifier.scopusauthorid | AokiKinoshita, KF=8704411700 | en_US |
dc.identifier.scopusauthorid | Furuta, K=7103345467 | en_US |