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Reports (Research Report) Year : 2004

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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Dates and versions

inria-00071440 , version 1 (23-05-2006)

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  • HAL Id : inria-00071440 , version 1

Cite

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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