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.
https://hal.inria.fr/inria-00119999
Contributor : Manuel Loth
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Submitted on : Tuesday, December 12, 2006 - 5:00:51 PM
Last modification on : Thursday, February 21, 2019 - 10:52:49 AM
Long-term archiving on : Wednesday, April 7, 2010 - 12:28:45 AM
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⟩
