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We present a class of regularized kernel methods for vector valued learning, which are based on filtering the spectrum of the kernel matrix. The considered methods include Tikhonov regularization as a special case, as well as interesting alternatives such as vector valued extensions of L2 boosting. ...

Early stopping is one of the most appealing heuristics when dealing with big data, since the computational resources required for learning are directly linked to the desired generalization properties. Interestingly, the theoretical foundations of learning with early stopping have only recently bee ...