(Not) bounding the true error

被引：0
|
作者：
Langford, J [1 ]
Caruana, R [1 ]
机构：
[1] Carnegie Mellon Univ, Dept Comp Sci, Pittsburgh, PA 15213 USA
来源：
ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 14, VOLS 1 AND 2 | 2002年 / 14卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
We present a new approach to bounding the true error rate of a continuous valued classifier based upon PAC-Bayes bounds. The method first constructs a distribution over classifiers by determining how sensitive each parameter in the model is to noise. The true error rate of the stochastic classifier found with the sensitivity analysis can then be tightly bounded using a PAC-Bayes bound. In this paper we demonstrate the method on artificial neural networks with results of a 2 - 3 order of magnitude improvement vs. the best deterministic neural net bounds.
引用
收藏
页码：809 / 816
页数：8
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