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The stochastic gradient method has become an algorithm of choice in machine learning, because of its simplicity and small computational cost, especially when dealing with big data sets. Despite its widespread use, the generalization properties of the variants of stochastic gradient method used in practice are relatively little understood. Most previous works consider generalization properties of SGM with only one pass over the data, while in practice multiple passes are usually considered. The effect of multiple passes has been studied extensively for the optimization of an empirical objective, but the role for generalization is less clear. In this talk, we start filling this gap studying the generalization properties of multiple passes stochastic gradient method for least square regression in an abstract non parametric setting. We show that, if all other parameters are fixed a priori, the number of passes over the data indeed acts as a regularization parameter. The obtained bounds are sharp and matches those obtained with other regularized techniques such as ridge regression.