Training Support Vector Machines using second order information

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The goal of the thesis is to solve the Support Vector Regression problem through second order methods to achieve fast convergence and good accuracy. A primal, unconstrained and twice differentiable formulation of the problem is used. The Representer theorem allows the use of kernels while operating in the primal.
Candidate: Alessio Sarullo
 Graduated April 2016