Learning vector quantization with training count (LVQTC)

Odorico, R

doi:10.1016/S0893-6080(97)00012-9

1997-08-01

Odorico, R

DOI：10.1016/S0893-6080(97)00012-9

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摘要

En 中文

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. (C) 1997 Elsevier Science Ltd.

Keyword:

learning vector quantization

neural network architecture

training

classification

high energy physics

elementary particle physics