Abstract
A neural net program for pattern classification is presented, which includes: i) an improved version of Kohonen's learning vector quantization (LVQ with training count); ii) feed-forward neural networks with back-propagation training; iii) Gaussian (or Mahalanobis distance) classification; iv) Fisher linear discrimination. Back-prop trainings with emulations of Intel's ETANN and Siemens' MA16 neural chips are available as options. The program has been developed for high energy physics applications.
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