Use of Multi Category Proximal SVM for Dataset Reduction

Vishwanathan, SVN and Murty, Narasimha M (2002) Use of Multi Category Proximal SVM for Dataset Reduction. [Book Chapter]

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Abstract

We present a tutorial introduction to Support Vector Machines (SVM) and try to show, using intuitive arguments, why SVM's tend to perform so well on a variety of challenging problems. We then discuss the quadratic optimization problem that arises as a result of the SVM formulation. We talk about a few computationally cheaper alternative formulations that have been developed recently. We go on to describe the Multi-category Proximal Support Vector Machines (MPSVM) in more detail. We propose a method for data set reduction by effective use of MPSVM. The linear MPSVM formulation is used in an iterative manner to identify the outliers in the data set and eliminate them. A k-Nearest Neighbor (k-NN) classifier is able to classify points using this reduced data set without significant loss of accuracy. We also present geometrically motivated arguments to justify our approach. Experiments on a few publicly available OCR data sets validate our claims.

Item Type:	Book Chapter
Publication:	Recent Advances in Intelligent Paradigms: Studies in Fuzziness and Soft Computing
Publisher:	Springer
Keywords:	Support Vector Machines;Statistical Learning Theory;VC dimension;Multi-category Proximal Support Vector Machines;Dataset Reduction
Department/Centre:	Division of Electrical Sciences > Electrical Engineering Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	01 Apr 2005
Last Modified:	19 Sep 2010 04:12
URI:	http://eprints.iisc.ac.in/id/eprint/103

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