Multi-category Bayesian Decision by Neural Networks

被引：0

作者：

Ito, Yoshifusa ^{[1
]}

Srinivasan, Cidambi ^{[2
]}

Izumi, Hiroyuki ^{[3
]}

机构：

[1] Aichi Med Univ, Sch Med, Nagakute, Aichi 4801195, Japan

[2] Univ Kentucky, Dept Stat, Lexington, KY 40506 USA

[3] Aichi Gakuin Univ, Dept Policy Sci, Aichi 4700195, Japan

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2008, PT I | 2008年 / 5163卷

关键词：

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

For neural networks, learning from dichotomous random samples is difficult. An example is learning of a Bayesian discriminant funciton. However, one-hidden-layer neural networks with fewer innner parameters can learn from such signals better thatn ordinary oens. We show that such neural networks can be used for approximating multi-category Bayesian discriminant functions when the state-conditional probability distributions are two dimensional normal distributions. Results of simple simulation are shown as examples.

引用

页码：21 / +

页数：2

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