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
Contributeur : Manuel Loth
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Soumis le : mardi 12 décembre 2006 - 17:00:51
Dernière modification le : mardi 5 juin 2018 - 10:14:21
Document(s) archivé(s) le : mercredi 7 avril 2010 - 00:28:45
