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Kernel methods and scale invariance using the triangular kernel

Abstract : We focus in this paper on the scale invariance of kernel methods using a particular function referred to as the triangular kernel. The study in (Sahbi and Fleuret, 2002) reported scale invariance for support vector machines (SVM) and the current work is an extension for support vector regression (SVR) and kernel principal component analysis (KPCA). First, we review these kernel methods and we illustrate analytically the scale invariance of the training processes. Experiments are conducted in several cases showing the scale invariance and the good performance in real pattern recognition problems including shape description, face detection and recognition.
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Contributor : Rapport de Recherche Inria <>
Submitted on : Tuesday, May 23, 2006 - 5:33:12 PM
Last modification on : Friday, December 18, 2020 - 10:30:03 AM
Long-term archiving on: : Sunday, April 4, 2010 - 10:14:06 PM


  • HAL Id : inria-00071440, version 1



Hichem Sahbi, François Fleuret. Kernel methods and scale invariance using the triangular kernel. [Research Report] RR-5143, INRIA. 2004. ⟨inria-00071440⟩



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