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Kernels for large margin time-series classification

Sivaramakrishnan, KR and Karthik, K and Bhattacharyya, Chiranjib (2007) Kernels for large margin time-series classification. In: International Joint Conference on Neural Networks, 12-17 Aug. 2007, Orlando, FL.

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In this paper we propose a novel family of kernels for multivariate time-series classification problems. Each time-series is approximated by a linear combination of piecewise polynomial functions in a Reproducing Kernel Hilbert Space by a novel kernel interpolation technique. Using the associated kernel function a large margin classification formulation is proposed which can discriminate between two classes. The formulation leads to kernels, between two multivariate time-series, which can be efficiently computed. The kernels have been successfully applied to writer independent handwritten character recognition.

Item Type: Conference Paper
Series.: IEEE International Joint Conference on Neural Networks
Publisher: IEEE
Additional Information: Copyright 2007 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 30 Mar 2010 12:03
Last Modified: 19 Sep 2010 05:57
URI: http://eprints.iisc.ac.in/id/eprint/26280

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