Abstract. We propose a randomized method for general convex optimization problems; namely, the minimization of a linear function over a convex body. The idea is to generate N rando...
Fabrizio Dabbene, P. S. Shcherbakov, Boris T. Poly...
Converged fabrics that support data, storage, and cluster networking in a unified fashion are desirable for their cost and manageability advantages. Recent trends towards higher-b...
Kevin Leigh, Parthasarathy Ranganathan, Jaspal Sub...
Abstract. In a previous paper, we have introduced an approach for extending both the terminological and the assertional part of a Description Logic knowledge base by using informat...
—The Support Vector Machine is a widely employed machine learning model due to its repeatedly demonstrated superior generalization performance. The Sequential Minimal Optimizatio...
Christopher Sentelle, Michael Georgiopoulos, Georg...
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decomposition of the value function. The MAXQ decomposition has both a procedural seman...