We give a universal kernel that renders all the regular languages linearly separable. We are not able to compute this kernel efficiently and conjecture that it is intractable, but...
RRL is a relational reinforcement learning system based on Q-learning in relational state-action spaces. It aims to enable agents to learn how to act in an environment that has no ...
We study the use of kernel subspace methods for learning low-dimensional representations for classification. We propose a kernel pooled local discriminant subspace method and com...
This paper presents a novel alternative approach, namely weakly supervised learning (WSL), to learn the pre-image of a feature vector in the feature space induced by a kernel. It ...
In this paper, we propose a novel nonparametric modeling technique, namely Space Kernel Analysis (SKA), as a result of the definition of the space kernel. We analyze the uncertai...