In this paper we introduce an online algorithm that uses integral reinforcement knowledge for learning the continuous-time optimal control solution for nonlinear systems with infinite horizon costs and partial knowledge of the system dynamics. This algorithm is a data based approach to the solution of the Hamilton-Jacobi-Bellman equation and it does not require explicit knowledge on the system’...

Approximate algorithms for structured prediction problems—such as the popular αexpansion algorithm (Boykov et al. 2001) in computer vision—typically far exceed their theoretical performance guarantees on realworld instances. These algorithms often find solutions that are very close to optimal. The goal of this paper is to partially explain the performance of α-expansion on MAP inference in Ferr...

This paper analyzes the influence of different parameters of Support Vector Machine (SVM) on text categorization performance. The research is carried out on different text collections and different subject headings (up to 1168 items). We show that parameter optimization can essentially increase text categorization performance. An estimation of range for searching optimal parameter is given. We ...

In this paper, we propose a novel Takagi-SugenoKang type interval-valued neural fuzzy system with asymmetric fuzzy membership functions (called TIVNFS-A). In addition, the corresponding type reduction procedure is integrated in the adaptive network layers to reduce the amount of computation in the system. Based on the Lyapunov stability theorem, the TIVNFS-A system is trained by the back-propag...

In this paper, we propose a power optimal opportunistic scheduling scheme for a multiuser single hop Time Division Multiple Access (TDMA) system. We formulate the problem of minimizing average transmission power subject to minimum rate constraints for individual users. We suggest a stochastic approximation based scheme to implement the policy and prove the convergence and stability of this algo...