We discuss how a large class of regularization methods, collectively known as spectral regularization and originally designed for solving illposed inverse problems, gives rise to regularized learning algorithms.All these algorithms are consistent kernel methods which can be easily implemented. The intuition behind their derivation is that the same principle allowing to numerically stabilize a matrix inversion problem * DISI, Università di Genova, v.
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