Model learning combined with dynamic programming has been shown to be e ective for learning control of continuous state dynamic systems. The simplest method assumes the learned mod...
Abstract. Recently, there has been an increasing interest in directed probabilistic logical models and a variety of languages for describing such models has been proposed. Although...
Jan Ramon, Tom Croonenborghs, Daan Fierens, Hendri...
- This paper presents a supervised learning based power management framework for a multi-processor system, where a power manager (PM) learns to predict the system performance state...
The present paper analyzes a learning experience run at University of Macerata, during a post degree course for in service teachers and mature students. The course was delivered e...
In this paper, we show that the LVQ learning algorithm converges to locally asymptotic stable equilibria of an ordinary differential equation. We show that the learning algorithm ...