Bayesian regularized neural network for multiple gene expression pattern classification

被引：0

作者：

Kelemen, A ^{[1
]}

Liang, WL ^{[1
]}

机构：

[1] Univ Mississippi, Dept Informat & Comp Sci, University, MS 38677 USA

来源：

PROCEEDINGS OF THE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS 2003, VOLS 1-4 | 2003年

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 developed Bayesian regularized neural network (BRNN) to characterize multiple gene expression temporal patterns from microarray experiments. One of its attractive property is that it takes into account both the high level noisy feature from microarray data and the uncertainties of the multiple models uniformly in order to avoid overfitting and to improve the generalization performance. Results am encouraging and comparison study with other popular methods is provided.

引用

页码：654 / 659

页数：6

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