Based on Bayesian Minimum Risk Matrix Euclidean Distance and Variance Calculation and Classification Application

Based on Bayesian Minimum Risk Matrix Euclidean Distance and Variance Calculation and Classification Application

Gang ChenBingcheng Qiu Jinpeng Liang 

Dept of Computer Science Guangdong Open University, Guangzhou 570091, China

School of Applied Mathematics, Guangdong University, Guangzhou 510075, China

Corresponding Author Email: 
gdchengang168@126.com
Page: 
190-195
|
DOI: 
https://doi.org/10.18280/ama_a.550402
Received: 
26 February 2018
|
Accepted: 
5 June 2018
|
Published: 
31 December 2018
| Citation

OPEN ACCESS

Abstract: 

The article mainly discussed the Euclidean distance based on Bayes minimum risk,variance calculation, on this basis probabilistic neural network model, and emphatically discusses the based on Bayes minimum risk, which based on the Euclidean distance and the calculation of variance for the probability of the advantages of neural network in classification. Combining the enterprise' poor for personnel research and development cause the shortcomings, a big electronics company in Dongguan of China research a sample training and analysis. The results show that the Euclidean distance based on Bayes minimum risk and variance calculation based on probabilistic neural network model are better than other traditional methods, and this method is effective.

Keywords: 

probability, research and development, Euclidean distance, variance

1. Introduction
2. Principle
3. Application
4. Result Analysis
5. Conclusions
  References

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