We study the problem of learning a kernel which minimizes a regularization error functional such as that used in regularization networks or support vector machines. We consider thi...
Andreas Argyriou, Charles A. Micchelli, Massimilia...
A general model is proposed for studying ranking problems. We investigate learning methods based on empirical minimization of the natural estimates of the ranking risk. The empiric...
This paper contributes to the theory of cutting planes for mixed integer linear programs (MILPs). Minimal valid inequalities are well understood for a relaxation of an MILP in tab...
A two-stage optimization methodology is proposed to solve the fixed-outline floorplanning problem that is a global optimization problem for wirelength minimization. In the first st...
—Body sensor networks are emerging as a promising platform for healthcare monitoring. These systems are composed of battery-operated embedded devices which process physiological ...
Hassan Ghasemzadeh, Nisha Jain, Marco Sgroi, Roozb...