| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1833037 | Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment | 2006 | 5 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Physics and Astronomy
Instrumentation
Authors
M. Feindt, U. Kerzel,