Although kernel measures of independence have been widely applied in machine learning (notably in kernel ICA), there is as yet no method to determine whether they have detected st...
Arthur Gretton, Kenji Fukumizu, Choon Hui Teo, Le ...
ABSTRACT. Estimating a non-uniformly sampled function from a set of learning points is a classical regression problem. Kernel methods have been widely used in this context, but eve...
Selecting relevant features for Support Vector Machine (SVM) classifiers is important for a variety of reasons such as generalization performance, computational efficiency, and ...
Features in many real world applications such as Cheminformatics, Bioinformatics and Information Retrieval have complex internal structure. For example, frequent patterns mined fr...
We present an extension to a recent method for learning of nonlinear manifolds, which allows to incorporate general cost functions. We focus on the -insensitive loss and visually d...