ESTIMATION OF MINIMUM MEASURE SETS IN REPRODUCING KERNEL HILBERT SPACES AND APPLICATIONS. - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2006

ESTIMATION OF MINIMUM MEASURE SETS IN REPRODUCING KERNEL HILBERT SPACES AND APPLICATIONS.

Abstract

Minimum measure sets (MMSs) summarize the information of a (single-class) dataset. In many situations, they can be preferred to estimated probability density functions (pdfs): they are strongly related to pdf level sets while being much easier to estimate in large dimensions. The main contribution of this paper is a theoretical connection between MMSs and one class Support Vector Machines. This justifies the use of one-class SVMs in the following applications: novelty detection (we give explicit convergence rate) and change detection.
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Dates and versions

inria-00119999 , version 1 (12-12-2006)

Identifiers

  • HAL Id : inria-00119999 , version 1

Cite

Manuel Davy, Frederic Desobry, Stephane Canu. ESTIMATION OF MINIMUM MEASURE SETS IN REPRODUCING KERNEL HILBERT SPACES AND APPLICATIONS.. IEEE ICASSP 2006, 2006, Toulouse, France. ⟨inria-00119999⟩
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