DocumentCode
2707853
Title
Neural-net method for dual subspace pattern recognition
Author
Xu, Lei ; Krzyzak, Adam ; Oja, Erkki
Author_Institution
Centre for Pattern Recognition & Machine Intelligence, Concordia Univ., Montreal, Que., Canada
fYear
1991
fDate
8-14 Jul 1991
Firstpage
379
Abstract
A novel version of the subspace pattern recognition method, called the dual subspace pattern recognition (DSPR) method, is proposed, and a neural-network model with a combination of modified Hebbian and anti-Hebbian learning rules is developed for implementing the DSPR method. An experimental comparison was made on an example data set by using this model and a three-layer forward net with back propagation learning. The results demonstrate that this model can outperform the back propagation model in some applications
Keywords
learning systems; neural nets; pattern recognition; Hebbian learning rules; anti-Hebbian learning rules; back propagation learning; dual subspace pattern recognition; neural-network model; three-layer forward net; Data analysis; Data compression; Data mining; Machine intelligence; Neural networks; Pattern recognition; Personal communication networks; Principal component analysis; Signal processing; Statistical analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 1991., IJCNN-91-Seattle International Joint Conference on
Conference_Location
Seattle, WA
Print_ISBN
0-7803-0164-1
Type
conf
DOI
10.1109/IJCNN.1991.155363
Filename
155363
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