We report on a generic uni- and bivariate algebraic kernel that is publicly available with Cgal 3.7. It comprises complete, correct, though efficient state-of-the-art implementati...
We propose a generic method for obtaining quickly good upper bounds on the minimal value of a multistage stochastic program. The method is based on the simulation of a feasible dec...
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...
This paper concerns learning binary-valued functions defined on IR, and investigates how a particular type of ‘regularity’ of hypotheses can be used to obtain better generali...
We present a classification algorithm built on our adaptation of the Generalized Lotka-Volterra model, well-known in mathematical ecology. The training algorithm itself consists ...
Karen Hovsepian, Peter Anselmo, Subhasish Mazumdar