DocumentCode
1749090
Title
A new online unsupervised learning rule for the BSB model
Author
Chartier, Sylvain ; Proulx, Robert
Author_Institution
Lab. d´´etudes en intelligence naturelle et artificiell, Univ. du Quebec a Montreal, Montreal, Que., Canada
Volume
1
fYear
2001
fDate
2001
Firstpage
448
Abstract
In this paper it is demonstrated that a new unsupervised learning rule enable a nonlinear model, like the BSB model and the Hopfield network, to learn online correlated stimuli. This rule stabilizes the weight matrix growth to the projection rule in a local fashion. The model has been tested with computer simulations that show that the model is stable over the variations of its free parameters and that it is noise tolerant in the recall task
Keywords
eigenvalues and eigenfunctions; neural nets; pattern recognition; real-time systems; unsupervised learning; BSB neural networks; eigenvalues; nonlinear model; pattern recognition; unsupervised learning; weight matrix; Artificial neural networks; Computer simulation; Eigenvalues and eigenfunctions; Hebbian theory; Hypercubes; Neural networks; Psychology; Testing; Unsupervised learning; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939061
Filename
939061
Link To Document