Journal
NUCLEAR INSTRUMENTS & METHODS IN PHYSICS RESEARCH SECTION A-ACCELERATORS SPECTROMETERS DETECTORS AND ASSOCIATED EQUIPMENT
Volume 559, Issue 1, Pages 190-194Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.nima.2005.11.166
Keywords
Bayes; neural network; classification; density reconstruction; data-mining; preprocessing
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Detailed analysis of correlated data plays a vital role in modern analyses. We present a sophisticated neural network package based on Bayesian statistics which can be used for both classification and event-by-event prediction of the complete probability density distribution for continuous quantities. The network provides numerous possibilities to automatically preprocess the input variables and uses advanced regularisation and pruning techniques to essentially eliminate the risk of overtraining. Examples from physics and industry are given. (c) 2005 Elsevier B.V. All rights reserved.
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