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In this work we are interested in the problems of supervised learning and variable se-lection when the input-output dependence is described by a nonlinear function depending on a few variables. Our go...
We tackle the problem of building adaptive estimation procedures for ill-posed inverse problems. For general regularization methods depending on tuning parameters, we construct a penalized method that...
Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables automatica...

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