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Nonlinear discrimination using support vector machine

conference contribution
posted on 2003-01-01, 00:00 authored by A Ali, Morshed ChowdhuryMorshed Chowdhury, S Subramanya
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.

History

Event

ISCA International Conference on Computers and Their Applications (18th: 2003: Honolulu, Hawaii)

Pagination

287 - 290

Publisher

International Society for Computers and Their Applications (ISCA)

Location

Honolulu, Hawaii

Place of publication

Cary, N.C.

Start date

2003-03-26

End date

2003-03-28

ISBN-13

9781880843468

ISBN-10

1880843463

Language

eng

Notes

RSD author affiliation changed for author Ali-GH 1/4/2011.

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

N Debnath

Title of proceedings

Computers and their applications: proceedings of the ISCA 18th international conference

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