In this paper we propose a random set framework for learning linguistic models for prediction problems. We show how we can model prediction problems based on learning linguistic p...
This paper presents an implementation methodology appropriate for providing a broad range of proven, classical Operations Research methods and techniques to the simulation modeler...
The performance of various Taylor model (TM)-based methods for the validated integration of ODEs is studied for some representative computational problems. For nonlinear problems, ...
The preference model introduced in this paper gives a natural framework and a principled solution for a broad class of supervised learning problems with structured predictions, su...
Identity uncertainty is the task of deciding whether two descriptions correspond to the same object. In this paper we discuss the identity uncertainty problem in the context of the...