The problem of learning metrics between structured data (strings, trees or graphs) has been the subject of various recent papers. With regard to the specific case of trees, some a...
Factored representations, model-based learning, and hierarchies are well-studied techniques for improving the learning efficiency of reinforcement-learning algorithms in large-sca...
Carlos Diuk, Alexander L. Strehl, Michael L. Littm...
Recent work on unsupervised feature learning has shown that learning on polynomial expansions of input patches, such as on pair-wise products of pixel intensities, can improve the...
Abstract. In this paper, we propose static and dynamic server selection techniques for multicast receivers who receive multiple streams from replicated servers. In the proposed sta...
As the size of available datasets in various domains is growing rapidly, there is an increasing need for scaling data mining implementations. Coupled with the current trends in co...