Bayes classifier based on self-organizing mapping neural network

Author

Zhu, Shuanghe ; Ma, Ling ; Lu, Hu

Author_Institution

Telecommun. Eng. Inst., Air Force Eng. Univ., Xi´´an, China

fYear

2000

fDate

2000

Firstpage

82

Lastpage

84

Abstract

This paper proposes a new self-organizing learning algorithm based on isoton mapping characteristic and cluster characteristic of self-organizing mapping neural network for the Bayes classification. The algorithm is that the network is trained by the given pattern samples, so that the classification results are directly presented from the output, avoiding the errors introduced by the estimation probability density function. The experimental results show the efficiency and reliability of this algorithm

Keywords

Bayes methods; learning (artificial intelligence); pattern classification; self-organising feature maps; Bayes classifier; cluster characteristic; isoton mapping characteristic; pattern samples; self-organizing learning algorithm; self-organizing mapping neural network; Clustering algorithms; Distribution functions; Estimation error; Neural networks; Neurons; Optimal matching; Parameter estimation; Probability density function; Probability distribution;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems, 2000. IEEE APCCAS 2000. The 2000 IEEE Asia-Pacific Conference on

Conference_Location

Tianjin

Print_ISBN

0-7803-6253-5

Type

conf

DOI

10.1109/APCCAS.2000.913411

Filename

913411