Abstract: Locally weighted learning (LWL) is a class of techniques from nonparametric statistics that provides useful representations and training algorithms for learning about com...
Stefan Schaal, Christopher G. Atkeson, Sethu Vijay...
Decades of research into the structure and function of the cerebellum have led to a clear understanding of many of its cells, as well as how learning might take place. Furthermore...
We develop the scheme of indefinite constraint databases using first-order logic as our representation language. When this scheme is instantiated with temporal constraints, the res...
This paper describes a new learning by example mechanism and its application for digital circuit design automation. This mechanism uses finite state machines to represent the infer...
Evolutionary computation has shown a great potential to work out several real-world problems in the point of optimization, but it is still quite far from realizing a system of matc...
This paper proposes a few steps to escape structured extensive representations for objects, in the context of evolutionary Topological Optimum Design (TOD) problems: early results ...
Abstract. In this paper, we describe how to realise alarm-correlation in cellular phone networks using extended logic programming which provides integrity constraints, implicit and...