Convex Hull Metrics and Neural Network Classifiers, Part 1

摘要：

This paper explores error estimation in feed-forward neural network classifiers from a geometrical perspective. Metrics, expressed as functions of the convex hull, are introduced and developed to capture the geometrical constraints across the single neuron classifier. An upper error bound is found as a function of convex hull jointedness and the underlying conditional probability distributions. A lower bound of the upper error bound is shown to be only a function of n, the number of samples used to form the convex hull.

作者: Jeff Willey Harold Szu Mona Zaghloul

作者单位: Code 5344, Naval Research Laboratory, Washington, DC 20375, USA ECE Dept, George Washington University, Washington, DC 20052, USA

会议类型: 国际会议

会议名称: 8th International Conference on Neural Information Processing(ICONIP 2001)(第八届国际神经信息处理大会)

会议地点: 上海

会议语种:英文

页码: 1143-1146

在线出版日期: 2001-11-14（万方平台首次上网日期，不代表论文的发表时间）