Guided by the goal of obtaining an optimization algorithm that is both fast and yields good generalization, we study the descent direction maximizing the decrease in generalizatio...
Nicolas Le Roux, Pierre-Antoine Manzagol, Yoshua B...
We study a class of algorithms that speed up the training process of support vector machines (SVMs) by returning an approximate SVM. We focus on algorithms that reduce the size of...
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
While many real-world combinatorial problems can be advantageously modeled and solved using Constraint Programming, scalability remains a major issue in practice. Constraint models...
Kenneth M. Bayer, Martin Michalowski, Berthe Y. Ch...
We propose an active set algorithm to solve the convex quadratic programming (QP) problem which is the core of the support vector machine (SVM) training. The underlying method is ...