Kernel supervised learning methods can be unified by utilizing the tools from regularization theory. The duality between regularization and prior leads to interpreting regularizat...
We determine the asymptotic behaviour of the function computed by support vector machines (SVM) and related algorithms that minimize a regularized empirical convex loss function i...
In a recent paper, Martens et al. introduced a specification mechanism for XML tree languages, based on rules of the form r s, where r, s are regular expressions. Sets of such ru...
We propose a new local learning scheme that is based on the principle of decisiveness: the learned classifier is expected to exhibit large variability in the direction of the test ...
We present a fast variational deconvolution algorithm that minimizes a quadratic data term subject to a regularization on the 1 -norm of the wavelet coefficients of the solution. ...