Title: Optimal nonlinear training in the multi-class proximity problem
Authors: Bollé, Désiré ×
Jongen, G
Shim, Gyoung Moo #
Issue Date: Nov-1996
Publisher: World Scientific
Series Title: International Journal of Neural Systems vol:7 issue:5 pages:655-664
Abstract: Using a signal-to-noise analysis, the effects of nonlinear modulation of the Hebbian learning rule in the multi-class proximity problem are investigated. Both random classification and classification provided by a Gaussian and a binary teacher are treated. Analytic expressions are derived for the learning and generalization rates around an old and a new prototype. For the proximity problem with binary inputs but Q'-state outputs, it is shown that the optimal modulation is a combination of a hyperbolic tangent and a linear function. As an illustration, numerical results are presented for the two-class and the Q' = 3 multi-class problem.
ISSN: 0129-0657
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Theoretical Physics Section
× corresponding author
# (joint) last author

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