From Data Topology to a Modular Classifier
Abstract
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.
Cite
@article{arxiv.0805.4290,
title = {From Data Topology to a Modular Classifier},
author = {Abdel Ennaji and Arnaud Ribert and Yves Lecourtier},
journal= {arXiv preprint arXiv:0805.4290},
year = {2008}
}