—An error in the theoretical derivations leading up to the main equation of the above correspondence by Prost et al. is pointed out, and corrections are presented. Due to the err...
Jonathan S. Maltz, Robert De Mello Koch, Andrew Wi...
In least squares support vector (LS-SVM), the key challenge lies in the selection of free parameters such as kernel parameters and tradeoff parameter. However, when a large number ...
— In this paper, we show that one-class SVMs can also utilize data covariance in a robust manner to improve performance. Furthermore, by constraining the desired kernel function ...
Support vector machines are trained by solving constrained quadratic optimization problems. This is usually done with an iterative decomposition algorithm operating on a small wor...
We propose a highly efficient framework for penalized likelihood kernel methods applied to multiclass models with a large, structured set of classes. As opposed to many previous a...