Reinforcement learning algorithms can become unstable when combined with linear function approximation. Algorithms that minimize the mean-square Bellman error are guaranteed to co...
— In this paper, we address the problem of FSM state assignment to minimize area and power. The objectives are targeted as single/independent as well as multi-objective optimizat...
In this paper, the minimization of incompletely specified multi-valued functions using functional decomposition is discussed. From the aspect of machine learning, learning sample...
Craig M. Files, Rolf Drechsler, Marek A. Perkowski
We study the convergence properties of an alternating proximal minimization algorithm for nonconvex structured functions of the type: L(x, y) = f(x)+Q(x, y)+g(y), where f : Rn → ...
— In large-scale fingerprinting localization systems, fine-grained location estimation and quick location determination are conflicting concerns. To achieve finer-grained loc...