Imprecision, incompleteness, prior knowledge or improved learning speed can motivate interval–represented data. Most approaches for SVM learning of interval data use local kernel...
Abstract. Discovering the topology of a set of labeled data in a Euclidian space can help to design better decision systems. In this work, we propose a supervised generative model ...
The eligibility trace is one of the most used mechanisms to speed up reinforcement learning. Earlier reported experiments seem to indicate that replacing eligibility traces would p...
In this work, we present a new model for a Recurrent Support Vector Machine. We call it intrinsic because the complete recurrence is directly incorporated within the considered opt...
Independent Variable Group Analysis (IVGA) is a method for grouping dependent variables together while keeping mutually independent or weakly dependent variables in separate group...
Abstract. We approach the themes “computing with chaos” and “reservoir computing” in a unified setting. Different neural architectures are mentioned which display chaotic...
Temporal coding is studied with an oscillatory network model that is a complex-valued generalization of the Cohen-Grossberg-Hopfield system. The model is considered with synchroni...
We apply kernel-based machine learning methods to online learning situations, and look at the related requirement of reducing the complexity of the learnt classifier. Online meth...