Nonlinear discrimination using support vector machine

Ali, A., Chowdhury, Morshed and Subramanya, S. 2003, Nonlinear discrimination using support vector machine, in Computers and their applications: proceedings of the ISCA 18th international conference, International Society for Computers and Their Applications (ISCA), Cary, N.C., pp. 287-290.

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Title Nonlinear discrimination using support vector machine
Author(s) Ali, A.
Chowdhury, Morshed
Subramanya, S.
Conference name ISCA International Conference on Computers and Their Applications (18th: 2003: Honolulu, Hawaii)
Conference location Honolulu, Hawaii
Conference dates 26-28 Mar. 2003
Title of proceedings Computers and their applications: proceedings of the ISCA 18th international conference
Editor(s) Debnath, Narayan
Publication date 2003
Start page 287
End page 290
Publisher International Society for Computers and Their Applications (ISCA)
Place of publication Cary, N.C.
Summary Appropriate training data always play an important role in constructing an efficient classifier to solve the data mining classification problem. Support Vector Machine (SVM) is a comparatively new approach in constructing a model/classifier for data analysis, based on Statistical Learning Theory (SLT). SVM utilizes a transformation of the basic constrained optimization problem compared to that of a quadratic programming method, which can be solved parsimoniously through standard methods. Our research focuses on SVM to classify a number of different sizes of data sets. We found SVM to perform well in the case of discrimination compared to some other existing popular classifiers.
Notes RSD author affiliation changed for author Ali-GH 1/4/2011.
ISBN 1880843463
9781880843468
Language eng
Field of Research 080299 Computation Theory and Mathematics not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30009587

Document type: Conference Paper
Collection: School of Information Technology
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