Abstract. Despite the efficiency shown by interior-point methods in large-scale linear programming, they usually perform poorly when applied to multicommodity flow problems. The ne...
We propose a general-purpose stochastic optimization algorithm, the so-called annealing stochastic approximation Monte Carlo (ASAMC) algorithm, for neural network training. ASAMC c...
This paper addresses the supervised learning in which the class membership of training data are subject to uncertainty. This problem is tackled in the framework of the Dempster-Sha...
The fat-tree is one of the topologies most widely used to build high-performance parallel computers. However, they are expensive and difficult to build. In this paper we propose t...
Abstract-- Planning resources for a supply chain is a major factor determining its success or failure. In this paper we introduce an Interval Type-2 Fuzzy Logic model of a distribu...
Simon Miller, Viara Popova, Robert John, Mario A. ...