Nonlinear Discrimination using Support Vector Machine
Abstract
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.
Recommended Citation
A. B. Ali et al., "Nonlinear Discrimination using Support Vector Machine," 18th International Conference on Computers and Their Applications 2003, CATA 2003, pp. 287 - 290, International Society for Computers and Their Applications (ISCA), Jan 2003.
Department(s)
Electrical and Computer Engineering
International Standard Book Number (ISBN)
978-161839549-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 International Society for Computers and Their Applicationc (ISCA), All rights reserved.
Publication Date
01 Jan 2003
