This paper presents a class of algorithms suitable for model reduction of distributed systems. Distributed systems are not suitable for treatment by standard model-reduction algor...
Recent research into single–objective continuous Estimation– of–Distribution Algorithms (EDAs) has shown that when maximum–likelihood estimations are used for parametric d...
In this paper, we consider the problem of supporting fault tolerance for adaptive and time-critical applications in heterogeneous and unreliable grid computing environments. Our g...
Abstract. The Minimum Description Length (MDL) is an informationtheoretic principle that can be used for model selection and other statistical inference tasks. One way to implement...
Abstract. Classical learning algorithms from the fields of artificial neural networks and machine learning, typically, do not take any costs into account or allow only costs depe...