Recently, we have introduced a novel approach to dynamic programming and reinforcement learning that is based on maintaining explicit representations of stationary distributions i...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...
Abstract -- We experimentally examine the performance of preconditioners based on entries of the symmetric positive definite part and small subspace solvers for linear system of eq...
Abstract. This paper presents a neural-evolutionary framework for the simulation of market models in a bounded rationality scenario. Each agent involved in the scenario make use of...
Background: Support Vector Machines (SVMs) provide a powerful method for classification (supervised learning). Use of SVMs for clustering (unsupervised learning) is now being cons...
This paper presents an algorithm that converges to points that satisfy a first order necessary condition of weakly Pareto solutions of multiobjective optimization problems. Hints ...