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Recognition of handwritten digits by combining independent learning vector quantizations

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1 Author(s)
Tin Kam Ho ; AT&T Bell Lab., Murray Hill, NJ, USA

Classifiers derived by learning vector quantization (LVQ) have well-defined decision regions that can be combined to construct a more accurate classifier. A given point may be included in a number of decision regions associated with different LVQ classifiers. The relative densities of classes in each region can be combined to obtain a final classification. The method allows useful inferences from small training sets, which is needed for problems involving large variations within each class. In an application of this method to the recognition of handwritten digits, it is shown that the classifier can be improved almost monotonically without suffering from over-adaptation to the training data

Published in:

Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on

Date of Conference:

20-22 Oct 1993