One-Class Support Vector Machines and Density Estimation: The Precise Relation
One-Class Support Vector Machines (SVM) afford the problem of estimating high density regions from univariate or multivariate data samples. To be more precise, sets whose probability is specified in advance are estimated. In this paper the exact relation between One-Class SVM and density estimation is demonstrated. This relation provides theoretical background for the behaviour of One-Class SVM when the Gaussian kernel is used, the only case for which successful results are shown in the literature.
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