Kernels for Vector-Valued Functions: a Review

Abstract

Kernel methods are among the most popular techniques in machine learning. From a frequentist/discriminative perspective they play a central role in regularization theory as they provide a natural choice for the hypotheses space and the regularization functional through the notion of reproducing kernel Hilbert spaces. From a Bayesian/generative perspective… (More)
DOI: 10.1561/2200000036

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@article{lvarez2012KernelsFV, title={Kernels for Vector-Valued Functions: a Review}, author={Mauricio A. {\'A}lvarez and Lorenzo Rosasco and Neil D. Lawrence}, journal={Foundations and Trends in Machine Learning}, year={2012}, volume={4}, pages={195-266} }