PATTERN-CLASSIFICATION BY THE BAYES MACHINE

被引：3

作者：

DIAMANTINI, C ^{[1
]}

SPALVIERI, A ^{[1
]}

机构：

[1] POLITECN MILAN,DIPARTIMENTO ELETTRON & INFORMAZ,I-20133 MILAN,ITALY

来源：

ELECTRONICS LETTERS | 1995年 / 31卷 / 24期

关键词：

BAYES METHOD; PATTERN CLASSIFICATION; VECTOR QUANTIZATION;

D O I：

10.1049/el:19951412

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

The pattern classification one has to test which class an observation vector belongs to, paying the minimum error probability. According to the nonparametric formulation, the statistical information is given by a labelled training set. The authors show how to design a classification rule by optimising the position of code vectors of a labelled vector quantiser under the minimum error probability criterion.

引用

页码：2086 / 2088

页数：3

共 6 条

[1]

KOHONEN T., BARNA G., CHRISLEY R., Statistical pattern recognition with neural networks: Benchmarking studies, Proc. IEEE Int. Conf. on Neural Networks, 1, pp. 61-68, (1988)

[2]

KOHONEN T., The self organizing map, Proc. IEEE, 78, 9, pp. 1464-1480, (1990)

[3]

FUKUNAGA K., Introduction to statistical pattern recognition, (1972)

[4]

DIAMANTINI C., Quantizzazione vettoriale adattativa con applicazione al riconoscimento statistico di pattern, (1995)

[5]

VAN TREES H.L., Detection, estimation, and modulation theory, (1971)

[6]

MCLEAN G.F., Vector quantization for texture classification, IEEE Trans., SMC-23, pp. 637-649, (1993)

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