This paper employs the network perspective to study patterns and structures of intraorganizational learning networks. The theoretical background draws from cognitive theories, the...
We describe three applications in computational learning theory of techniques and ideas recently introduced in the study of parameterized computational complexity. (1) Using param...
Rodney G. Downey, Patricia A. Evans, Michael R. Fe...
Few attempts have been made to investigate the utility of temporal reasoning within machine learning frameworks for temporal relation classification between events in news articl...
Abstract. The ability to recognize human activities from sensory information is essential for developing the next generation of smart devices. Many human activity recognition tasks...
Niels Landwehr, Bernd Gutmann, Ingo Thon, Luc De R...
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 ...