A multitask learning framework is developed for discriminative classification and regression where multiple large-margin linear classifiers are estimated for different predictio...
The choice of the kernel function which determines the mapping between the input space and the feature space is of crucial importance to kernel methods. The past few years have se...
This paper presents a novel approach for landmarkbased shape deformation, in which fitting error and shape difference are formulated into a support vector machine (SVM) regression...
Many speaker verification (SV) systems combine multiple classifiers using score-fusion to improve system performance. For SVM classifiers, an alternative strategy is to combine...
In this paper we eliminate the need for parameter estimation associated with the set covering machine (SCM) by directly minimizing generalization error bounds. Firstly, we consider...