Abstract
In this paper we describe a method for data set reduction by effective use of Multi-category Proximal Support Vector Machine (MPSVM). By using the Linear MPSVM Formulation in an iterative manner we 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 present experiments on a well known large OCR data set to validate our claims.
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© 2002 Springer-Verlag Berlin Heidelberg
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Vishwanathan, S.V.N., Murty, M.N. (2002). Use of Multi-category Proximal SVM for Data Set Reduction. In: Abraham, A., Köppen, M. (eds) Hybrid Information Systems. Advances in Soft Computing, vol 14. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1782-9_2
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DOI: https://doi.org/10.1007/978-3-7908-1782-9_2
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1480-4
Online ISBN: 978-3-7908-1782-9
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