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Using an Hebbian learning rule for multi-class SVM classifiers.

Thierry Viéville 1 Sylvie Crahay 1
1 ODYSSEE - Computer and biological vision
DI-ENS - Département d'informatique de l'École normale supérieure, CRISAM - Inria Sophia Antipolis - Méditerranée , ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt, ENPC - École des Ponts ParisTech
Abstract : Regarding biological visual classification, recent series of experiments have enlighten the fact that data classification can be realized in the human visual cortex with latencies of about 100-150 ms, which, considering the visual pathways latencies, is only compatible with a very specific processing architecture, described by models from Thorpe et al. Surprisingly enough, this experimental evidence is in coherence with algorithms derived from the statistical learning theory. More precisely, there is a double link: on one hand, the so-called Vapnik theory offers tools to evaluate and analyze the biological model performances and on the other hand, this model is an interesting front-end for algorithms derived from the Vapnik theory. The present contribution develops this idea, introducing a model derived from the statistical learning theory and using the biological model of Thorpe et al. We experiment its performances using a restrained sign language recognition experiment. This paper intends to be read by biologist as well as statistician, as a consequence basic material in both fields have been reviewed.
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https://hal.inria.fr/inria-00000030
Contributor : Thierry Viéville <>
Submitted on : Monday, May 16, 2005 - 4:34:13 PM
Last modification on : Tuesday, September 22, 2020 - 3:57:59 AM
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Thierry Viéville, Sylvie Crahay. Using an Hebbian learning rule for multi-class SVM classifiers.. Journal of Computational Neuroscience, Springer Verlag, 2004. ⟨inria-00000030⟩

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