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Extension of TSVM to multi-class and hierarchical text classification problems With general losses

Selvaraj, Sathiya Keerthi and Sellamanickam, Sundararajan and Shevade, Shirish (2012) Extension of TSVM to multi-class and hierarchical text classification problems With general losses. In: 24th International Conference on Computational Linguistics, 2012, Dec 8-15, 2012 , Mumbai, India.

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Abstract

Transductive SVM (TSVM) is a well known semi-supervised large margin learning method for binary text classification. In this paper we extend this method to multi-class and hierarchical classification problems. We point out that the determination of labels of unlabeled examples with fixed classifier weights is a linear programming problem. We devise an efficient technique for solving it. The method is applicable to general loss functions. We demonstrate the value of the new method using large margin loss on a number of multi-class and hierarchical classification datasets. For maxent loss we show empirically that our method is better than expectation regularization/constraint and posterior regularization methods, and competitive with the version of entropy regularization method which uses label constraints.

Item Type: Conference Paper
Additional Information: Copyright of this article belongs to Curran Associates, Inc.
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Francis Jayakanth
Date Deposited: 19 Nov 2013 06:34
Last Modified: 19 Nov 2013 06:34
URI: http://eprints.iisc.ac.in/id/eprint/47808

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