Abstract
A neural net program for pattern classification is presented. The neural net architecture is based on an improved version of Kohonen's learning vector organization: learning vector quantization with training count. The number of times a neuron is trained by input patterns of each class is stored in newly introduced training counters. This information, together with other which is collected during training, is used at the end of each training epoch for pruning, merging and creating neurons, and also in the classification process to estimate the reliability of the classification. Initially, neurons are automatically allocated to classes in proportion to the volumes and linear sizes of the corresponding distributions of input patterns in the pattern space, as estimated from the class covariance matrices. As an aside, pattern classification according to Mahalanobis distance and Fisher linear discrimination is also provided
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