A wrapper approach with support vector machines for text categorization

Authors:
E. Montañés;J. R. Quevedo;I. Díaz
Affiliations:
Artificial Intelligence Center, University of Oviedo, Asturias, Spain;Artificial Intelligence Center, University of Oviedo, Asturias, Spain;Artificial Intelligence Center, University of Oviedo, Asturias, Spain
Venue:
IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1
Year:
2003

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Abstract

The ScanningN-T uple classifier (SNT) was introduced by Lucas and Amiri [1, 2] as an efficient and accurate classifier for chain-coded hand-written digits. The SNT operates as speeds of tens of thousands of sequences per second, during both the trainingand the recognition phases. The main contribution of this paper is to present a new discriminative trainingrule for the SNT. Two versions of the rule are provided, based on minimizingthe mean-squared error and the cross-entropy, respectively. The discriminative trainingrule offers improved accuracy at the cost of slower trainingtime, since the trainingis now iterative instead of single pass. The cross-entropy trained SNT offers the best results, with an error rate of 2.5% on sequences derived from the MNIST test set.