New neural network realization algorithm for Neyman-Pearson criterion in hypothesis testing

被引：0
|
作者：
Zhang, Z.L. [1 ]
Sun, S.H. [1 ]
机构：
[1] Dep. of Auto. Test and Control, Harbin Inst. of Technol., Harbin 150001, China
来源：
| 2001年 / Harbin Institute of Technology卷 / 33期
关键词：
Algorithms - Data processing - Neural networks - Probability distributions;
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暂无
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学科分类号：
摘要：
The Neyman-Pearson criterion in hypothesis testing is based on the probability rate for problems such as classification, detection, and pattern recognition as an improved kind of non-least-square learning algorithm to decide the criterion of the probability distribution. An algorithm based on the absolute error is given. Simulation results show that the new algorithm has less error and is more suitable for Neyman-Pearson criterion.
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