1 research outputs found
Non-asymptotic Analysis of -norm Support Vector Machines
Support Vector Machines (SVM) with penalty became a standard tool in
analysis of highdimensional classification problems with sparsity constraints
in many applications including bioinformatics and signal processing. Although
SVM have been studied intensively in the literature, this paper has to our
knowledge first non-asymptotic results on the performance of -SVM in
identification of sparse classifiers. We show that a -dimensional -sparse
classification vector can be (with high probability) well approximated from
only Gaussian trials. The methods used in the proof include
concentration of measure and probability in Banach spaces