DocumentCode :
323389
Title :
A selective attention template matching neural network
Author :
Ye Xiangyun ; Feihu, Qi ; Hujun, Yin
Author_Institution :
Dept. of Comput. Sci. & Eng., Shanghai Jiaotong Univ., China
Volume :
1
fYear :
1997
fDate :
28-31 Oct 1997
Firstpage :
507
Abstract :
A kind of similarity measure with a selective attentional property is proposed. The neural network (selective attentional template matching network, SATMN) model built with the proposed measure can be applied to template matching and classification. The model is composed of matching subnets and competing subnets, in which comparison and classification are carried out respectively. The similarity measure is available to both discrete and analog patterns. The experimental results show that it takes advantage of several widely used similarity measures in describing the variance between patterns. The neural network implementation and experimental results are given
Keywords :
learning (artificial intelligence); neural nets; pattern classification; pattern matching; SATMN; analog patterns; discrete patterns; experimental results; learning; pattern classification; pattern variance; selective attention template matching neural network; similarity measure; template matching; Electronic learning; Humans; Neural networks; Pattern classification;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Processing Systems, 1997. ICIPS '97. 1997 IEEE International Conference on
Conference_Location :
Beijing
Print_ISBN :
0-7803-4253-4
Type :
conf
DOI :
10.1109/ICIPS.1997.672834
Filename :
672834
Link To Document :
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