Co-Training on Handwritten Digit Recognition

Authors:
Jun Du;Charles X. Ling
Affiliations:
Department of Computer Science, The University of Western Ontario, London, Canada N6A 5B7;Department of Computer Science, The University of Western Ontario, London, Canada N6A 5B7
Venue:
Canadian AI '09 Proceedings of the 22nd Canadian Conference on Artificial Intelligence: Advances in Artificial Intelligence
Year:
2009

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Abstract

In this paper, we apply a semi-supervised learning paradigm -- co-training to handwritten digit recognition, so as to construct high-performance recognition model with very few labeled images. Experimental results show that, based on arbitrary two types of given features, co-training can always achieve high accuracy. Thus, it provides a generic and robust approach to construct high performance model with very few labeled handwritten digit images.