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

Manuel Davy 1, 2 Frederic Desobry 3 Stephane Canu 4
1 SEQUEL - Sequential Learning
LIFL - Laboratoire d'Informatique Fondamentale de Lille, LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal, Inria Lille - Nord Europe
2 LAGIS-SI
LAGIS - Laboratoire d'Automatique, Génie Informatique et Signal
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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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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