  This paper proposes the adaptation of Support Vector Data Description (SVDD)
to the multiple kernel case (MK-SVDD), based on SimpleMKL. It also introduces a
variant called Slim-MK-SVDD that is able to produce a tighter frontier around
the data. For the sake of comparison, the equivalent methods are also developed
for One-Class SVM, known to be very similar to SVDD for certain shapes of
kernels.
  Those algorithms are illustrated in the context of 3D-shapes filtering and
outliers detection. For the 3D-shapes problem, the objective is to be able to
select a sub-category of 3D-shapes, each sub-category being learned with our
algorithm in order to create a filter. For outliers detection, we apply the
proposed algorithms for unsupervised outliers detection as well as for the
supervised case.
