Please use this identifier to cite or link to this item:
Title: An Implementation of Support Vector Machines on Iris Dataset
Authors: Timotius, Ivanna K.
Setyawan, Iwan
Febrianto, Andreas A.
Keywords: pattern recognition;support vector machines;kernel function
Issue Date: Nov-2009
Publisher: Institut Teknologi Bandung
Abstract: Support Vector Machines (SVM) is a set of related supervised learning method used for classification. SVM is used to construct a hyperplane as the decision surface in such a way that the margin of separation between positive and negative examples is maximized. By default, this hyperplane is linear. To improve the classification performance, it is desirable to use a non-linear hyperplane. In order to construct a non-linear hyperplane using SVM, we use kernel functions. This paper presents a comparison of using several kernel functions in the SVM algorithm for Iris dataset classification.
Description: The 5th International Conference on Telematics System, Services and Applications 19-21 nov 2009 Telecommunication Engineering Scientific and Research Group School of Electrical Engineering and Informatics Institut Teknologi Bandung : 39-41
ISSN: 1693-993x
Appears in Collections:Published Research Reports

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.