Results are presented of a simulation which mimics an evolutionary learning process for small networks. Special features of these networks include a high recurrency, a transition ...
In this paper, we propose a recursive method for structural learning of directed acyclic graphs (DAGs), in which a problem of structural learning for a large DAG is first decompos...
We address the problem of learning classifiers using several kernel functions. On the contrary to many contributions in the field of learning from different sources of information...
Matthieu Kowalski, Marie Szafranski, Liva Ralaivol...
This paper describes an approach to robotic control that is patterned after models of human skill acquisition. The intent is to develop robots capable of learning how to accomplis...
We propose a new neural network architecture, called Simple Recurrent Temporal-Difference Networks (SR-TDNs), that learns to predict future observations in partially observable en...