This paper describes a method of supervised learning based on forward selection branching. This method improves fault tolerance by means of combining information related to general...
We derive an optimal learning rule in the sense of mutual information maximization for a spiking neuron model. Under the assumption of small fluctuations of the input, we find a s...
We study the generative development of control programs for families of embedded devices. A software family is described by a single common model and restriction specifications for...
This paper addresses the issues related to the decision processes of manufacturing system simulation. The manufacturing system is perceived in terms of intelligent entities capabl...
The goal of transfer learning is to improve the learning of a new target concept given knowledge of related source concept(s). We introduce the first boosting-based algorithms for...