Using an Hebbian learning rule for multi-class SVM classifiers
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 processin...
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| Veröffentlicht in: | Journal of computational neuroscience 2004-11, Vol.17 (3), p.271-287 |
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| container_title | Journal of computational neuroscience |
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| creator | Viéville, Thierry Crahay, Sylvie |
| description | 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. |
| doi_str_mv | 10.1023/B:JCNS.0000044873.20850.9c |
| format | Article |
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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. 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| subjects | Adaptation and Self-Organizing Systems Algorithms Documentation Humans Learning - physiology Life Sciences Models, Neurological Models, Psychological Nonlinear Sciences Other Reaction Time - physiology Recognition (Psychology) - physiology Sign Language Visual Cortex - physiology Visual Pathways - physiology |
| title | Using an Hebbian learning rule for multi-class SVM classifiers |
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