Logarithmic Regret Algorithms for Strongly Convex Repeated Games

Abstract

Many problems arising in machine learning can be cast as a convex optimization problem, in which a sum of a loss term and a regularization term is minimized. For example, in Support Vector Machines the loss term is the average hinge-loss of a vector over a training set of examples and the regularization term is the squared Euclidean norm of this vector. In… (More)

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