High Quality Content by WIKIPEDIA articles! A Bayesian interpretation of regularization for kernel methods is often useful. Kernel methods are central to both the regularization and the Bayesian point of views in machine learning. In regularization they are a natural choice for the hypotheses space and the regularization functional through the notion of reproducing kernel Hilbert spaces. In Bayesian probability they are a key component of Gaussian processes, where the kernel function is known as the covariance function. Kernel methods have traditionally been used in supervised learning problems where the input space is usually a space of vectors while the output space is a space of scalars. More recently these methods have been extended to problems that deal with multiple outputs such as in multi-task learning.