  Within a statistical learning setting, we propose and study an iterative
regularization algorithm for least squares defined by an incremental gradient
method. In particular, we show that, if all other parameters are fixed a
priori, the number of passes over the data (epochs) acts as a regularization
parameter, and prove strong universal consistency, i.e. almost sure convergence
of the risk, as well as sharp finite sample bounds for the iterates. Our
results are a step towards understanding the effect of multiple epochs in
stochastic gradient techniques in machine learning and rely on integrating
statistical and optimization results.
