Abstract
Pegasos is a popular and reliable machine learning algorithm for making linear Support Vector Machines solvable at the larger scale. It benefits from the strongly convex optimization objective, faster convergence rates and lower computational and memory costs. In this paper we devise a new weighted formulation of the Pegasos algorithm which favors from the different coordinate-wise λ i regularization parameters. Together with the proposed extension we give a brief theoretical justification of its convergence to an optimal solution and analyze at a glance its computational costs. We conclude our paper with the numerical results obtained for UCI datasets and demonstrate the merits and the importance of our approach for achieving a better classification accuracy and convergence rates in the partially or fully stochastic setting.
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