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Title: Efficient Support Vector Machine Classification Using Prototype Selection and Generation
Authors: Ougiaroglou, Stefanos
Diamantaras, Konstantinos I.
Evangelidis, Georgios
Editors: Iliadis, Lazaros
Maglogiannis, Ilias
Type: Conference Paper
Subjects: FRASCATI::Natural sciences::Computer and information sciences
Keywords: Support Vector Machines
k-NN classification
Data reduction
Prototype abstraction
Prototype generation
Issue Date: 2016
Volume: 475
First Page: 328
Last Page: 340
Volume Title: Artificial Intelligence Applications and Innovations
Part of Series: IFIP Advances in Information and Communication Technology
Part of Series: IFIP Advances in Information and Communication Technology
Abstract: Although Support Vector Machines (SVMs) are considered effective supervised learning methods, their training procedure is time-consuming and has high memory requirements. Therefore, SVMs are inappropriate for large datasets. Many Data Reduction Techniques have been proposed in the context of dealing with the drawbacks of k-Nearest Neighbor classification. This paper adopts the concept of data reduction in order to cope with the high computational cost and memory requirements in the training process of SVMs. Experimental results illustrate that Data Reduction Techniques can effectively improve the performance of SVMs when applied as a preprocessing step on the training data.
ISBN: 978-3-319-44943-2
ISSN: 1868-4238
Other Identifiers: 10.1007/978-3-319-44944-9_28
Appears in Collections:Department of Applied Informatics

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