期刊
出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.nima.2005.11.166
关键词
Bayes; neural network; classification; density reconstruction; data-mining; preprocessing
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据