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dc.contributor.authorTimotius, Ivanna K.
dc.contributor.authorSetyawan, Iwan
dc.contributor.authorFebrianto, Andreas A.
dc.descriptionThe 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 : 39-41en_US
dc.description.abstractSupport 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.en_US
dc.publisherInstitut Teknologi Bandungen_US
dc.subjectpattern recognitionen_US
dc.subjectsupport vector machinesen_US
dc.subjectkernel functionen_US
dc.titleAn Implementation of Support Vector Machines on Iris Dataseten_US
Appears in Collections:Published Research Reports

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