W e apply distance based classifiers in the context of a content based image retrieval task in dermatology. In the present project, only RGB color information is used. We employ two different methods in order to obtain a discriminative distance measure for classification and retrieval: Generalized Matrix LVQ (GMLVQ) and the Large Margin Nearest Neighbor (LMNN) approach. Both methods provide a l...

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier t...

We study the problem of learning large margin halfspaces in various settings using coresets and show that coresets are a widely applicable tool for large margin learning. A large margin coreset is a subset of the input data sufficient for approximating the true maximum margin solution. In this work, we provide a direct algorithm and analysis for constructing large margin coresets. We show vario...

Margin-based classifiers have been popular in both machine learning and statistics for classification problems. Since a large number of are available, one natural question is which type should be used given particular task. We answer this by investigating the asymptotic performance family large-margin under two component mixture models situations where data dimension $p$ sample $n$ large. This ...