Classification of mouse chromosomes using artificial neural networks

M.T Musavi,M.T Davisson,M Qiao,E.C Akeson

doi:10.1109/icnn.1996.549008

Classification of mouse chromosomes using artificial neural networks

M.T Musavi, M.T Davisson + Show 2 more

https://doi.org/10.1109/icnn.1996.549008

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Publication Date: Jun 3, 1996

Citations: 1

Affiliation: University of Maine

#Radial Basis Function #Fast Orthogonal Search + Show 8 more

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Abstract

This paper presents the results of our experiments for classification of mouse chromosomes using a radial basis function (RBF) and a probabilistic neural network (PNN). The fast orthogonal search (FOS) was utilized for training of the RBF network. There were 840 training chromosomes and 540 testing chromosomes. The best classification error rate was recorded at 16.4% for the RBF network. This result is better than the best available result of 18.3% which was achieved with much more training chromosomes.

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