Although support vector machines (SVMs) for binary classification give rise to a decision rule that only relies on a subset of the training data points (support vectors), it will ...
Antoni B. Chan, Nuno Vasconcelos, Gert R. G. Lanck...
Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of l...
We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Inde...
Le Song, Alexander J. Smola, Arthur Gretton, Karst...
We propose a method for the classification of matrices. We use a linear classifier with a novel regularization scheme based on the spectral 1-norm of its coefficient matrix. The s...
Quadratic program relaxations are proposed as an alternative to linear program relaxations and tree reweighted belief propagation for the metric labeling or MAP estimation problem...