Mouse chromosome classification by radial basis function network with fast orthogonal search

This paper provides the results of our study on automatic classification of mouse chromosomes. A radial basis function neural network was compared with a multi-layer perceptron and a probabilistic neural network. The networks were trained and tested with 3723 chromosomes presented to each network as 30-point banding profiles. The radial basis function classifier trained with the fast orthogonal search learning rule provided the best unconstrained classification error rate of 12.7% which was obtained with a training set of 2250 chromosomes.