Abstract. In [8] Yamauchi and Beer explored the abilities of continuous time recurrent neural networks (CTRNNs) to display reinforcementlearning like abilities. The investigated ta...
We present a model of creating a hierarchical set of rules that encode generalizations and exceptions derived from induction learning. The rules use the input features directly an...
The learning of probabilistic models with many hidden variables and nondecomposable dependencies is an important and challenging problem. In contrast to traditional approaches bas...
In this paper, we describe a cooperative transportation problem with two humanoid robots and introduce a machine learning approach to solving the problem. The difficulty of the ta...
Abstract. In this paper we elaborate on a kernel extension to tensorbased data analysis. The proposed ideas find applications in supervised learning problems where input data have ...
Marco Signoretto, Lieven De Lathauwer, Johan A. K....