We give a universal kernel that renders all the regular languages linearly separable. We are not able to compute this kernel efficiently and conjecture that it is intractable, but...
Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is perfor...
Gert R. G. Lanckriet, Nello Cristianini, Peter L. ...
A multitask learning framework is developed for discriminative classification and regression where multiple large-margin linear classifiers are estimated for different predictio...
This paper is based on a new way for determining the regularization trade-off in least squares support vector machines (LS-SVMs) via a mechanism of additive regularization which ha...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...
When constructing a classifier, the probability of correct classification of future data points should be maximized. We consider a binary classification problem where the mean and...
Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib...