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.
Nature Inspiration for Support Vector Machines
ANGUITA, DAVIDE;
2006-01-01
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.
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