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