From Data Topology to a Modular Classifier

Details From Data Topology to a Modular Classifier From Data Topology to a Modular Classifier

2008-05-28

arxiv.org/abs/0805.4290v1

International Journal On Document Analysis and Recognition 6, 1 (2003) 1-9

Computer Science

Learning

Scientific paper

10.1007/s10032-002-0095-3

This article describes an approach to designing a distributed and modular neural classifier. This approach introduces a new hierarchical clustering that enables one to determine reliable regions in the representation space by exploiting supervised information. A multilayer perceptron is then associated with each of these detected clusters and charged with recognizing elements of the associated cluster while rejecting all others. The obtained global classifier is comprised of a set of cooperating neural networks and completed by a K-nearest neighbor classifier charged with treating elements rejected by all the neural networks. Experimental results for the handwritten digit recognition problem and comparison with neural and statistical nonmodular classifiers are given.

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