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ICMCS
2005
IEEE

Conditionally Positive Definite Kernels for SVM Based Image Recognition

13 years 9 months ago
Conditionally Positive Definite Kernels for SVM Based Image Recognition
Kernel based methods such as Support Vector Machine (SVM) have provided successful tools for solving many recognition problems. One of the reason of this success is the use of kernels. Positive definiteness has to be checked for kernels to be suitable for most of these methods. For instance for SVM, the use of a positive definite kernel insures that the optimized problem is convex and thus the obtained solution is unique. Alternative class of kernels called conditionally positive definite have been studied for a long time from the theoretical point of view and have drawn attention from the community only in the last decade. We propose a new kernel, named log kernel, which seems particularly interesting for images. Moreover, we prove that this new kernel is a conditionally positive definite kernel as well as the power kernel. Finally, we show from experimentations that using conditionally positive definite kernels allows us to outperform classical positive definite kernels.
Sabri Boughorbel, Jean-Philippe Tarel, Nozha Bouje
Added 24 Jun 2010
Updated 17 Dec 2010
Type Conference
Year 2005
Where ICMCS
Authors Sabri Boughorbel, Jean-Philippe Tarel, Nozha Boujemaa
http://perso.lcpc.fr/tarel.jean-philippe/publis/icme05.html
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