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A regularized linear classifier for effective text classification

Nandanwar, Sharad and Murty, Narasimha M (2012) A regularized linear classifier for effective text classification. In: 19th International Conference, ICONIP 2012, November 12-15, 2012, Doha, Qatar.

ICONIP_218-225_2012.pdf - Accepted Version

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Official URL: http://dx.doi.org/10.1007/978-3-642-34481-7_27


In document community support vector machines and naïve bayes classifier are known for their simplistic yet excellent performance. Normally the feature subsets used by these two approaches complement each other, however a little has been done to combine them. The essence of this paper is a linear classifier, very similar to these two. We propose a novel way of combining these two approaches, which synthesizes best of them into a hybrid model. We evaluate the proposed approach using 20ng dataset, and compare it with its counterparts. The efficacy of our results strongly corroborate the effectiveness of our approach.

Item Type: Conference Proceedings
Series.: Lecture Notes in Computer Science
Publisher: Springer Berlin Heidelberg
Additional Information: Copyright of this article belongs to Springer Berlin Heidelberg.
Keywords: Support Vector Machine; Naïve Bayes Classifier; Regularization
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 02 Jul 2013 08:28
Last Modified: 04 Jul 2013 10:11
URI: http://eprints.iisc.ac.in/id/eprint/46556

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