Risk Bounds for Regularized Least-squares Algorithm with Operator-valued kernels
Abstract : We show that recent results in [3] on risk bounds for regularized least-squares on reproducing kernel Hilbert spaces can be straight-forwardly extended to the vector-valued regression setting. We first briefly introduce central concepts on operator-valued kernels, then we show how risk bounds can be expressed in terms of a generalization of effective dimension.