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Iterative regularization is a classic idea in regularization theory, that hasrecently become popular in machine learning. On the one hand, it allows todesign efficient algorithms controlling at the same time numerical andstatistical accuracy. On the other hand it allows to shed light on the learningcurves observed while training neural networks. In this paper, we focus oniterative regularization in the context of classification. After contrastingthis setting with that of regression and inverse problems, we develop aniterative regularization approach based on the use of the hinge loss function.More precisely we consider a diagonal approach for a family of algorithms forwhich we prove convergence as well as rates of convergence. Our approachcompares favorably with other alternatives, as confirmed also in numericalsimulations.

Iterative regularization in classification via hinge loss diagonal descent