Approximate RBF Kernel SVM and Its Applications in Pedestrian Classification

Abstract : This paper presents an efficient approximation to the nonlinear SVM with Radial Basis Function (RBF) kernel. By employing second-order polynomial approximation to RBF kernel, the derived approximate RBF-kernel SVM classifier can take a compact form by exchanging summation in conventional SVM classification formula, leading to constant low complexity that is only relevant to the dimensions of feature. Experiments on pedestrian classification show that our approximate RBF-kernel SVM achieved classification performance close to the exact implementation with significantly low time and memory.
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Communication dans un congrès
The 1st International Workshop on Machine Learning for Vision-based Motion Analysis - MLVMA'08, Oct 2008, Marseille, France. 2008
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Hui Cao, Takashi Naito, Yoshiki Ninomiya. Approximate RBF Kernel SVM and Its Applications in Pedestrian Classification. The 1st International Workshop on Machine Learning for Vision-based Motion Analysis - MLVMA'08, Oct 2008, Marseille, France. 2008. 〈inria-00325810〉

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