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EnglishWe propose in this paper a new kernel, suited for Support Vector Machines learning, which is inspired from the biological world. The kernel is based on Gabor filters that are a good model for the response of the cells in the primary visual cortex and have been shown to be very effective in processing natural images. Furthermore, we build a link between energy-efficiency, which is a driving force in biological processing systems, and good generalization ability of learning machines. This connection can be the starting point for developing new kernel-based learning algorithms.
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| Titolo: | Nature Inspiration for Support Vector Machines | |
| Autori: | ||
| Data di pubblicazione: | 2006 | |
| Abstract: | We propose in this paper a new kernel, suited for Support Vector Machines learning, which is inspired from the biological world. The kernel is based on Gabor filters that are a good model for the response of the cells in the primary visual cortex and have been shown to be very effective in processing natural images. Furthermore, we build a link between energy-efficiency, which is a driving force in biological processing systems, and good generalization ability of learning machines. This connection can be the starting point for developing new kernel-based learning algorithms. | |
| Handle: | http://hdl.handle.net/11567/536345 | |
| ISBN: | 9783540465379 9783540465393 | |
| Appare nelle tipologie: | 04.01 - Contributo in atti di convegno |
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