title: Learning Vector Quantization with Training Count (LVQTC).
abstract: Kohonen's learning vector quantization (LVQ) is modified by attributing training counters to each neuron, which record its training statistics. During training, this allows for dynamic self-allocation of the neurons to classes. In the classification stage training counters provide an estimate of the reliability of classification of the single neurons, which can be exploited to obtain a substantially higher purity of classification. The method turns out to be especially valuable in the presence of considerable overlaps among class distributions in the pattern space. The results of a typical application to high energy elementary particle physics are discussed in detail. Copyright 1997 Elsevier Science Ltd.
authors.affiliation: University of Bologna, Istituto Nazionale di Fisica Nucleare, Italy
authors.forename: Roberto
authors.initials: R
authors.lastname: Odorico