As part of a National Science Foundation-funded study, we analyze data from a 2002 Midwest urban random sample survey (n = 167 adults) of differential access to and uses of comput...
Supervised estimation methods are widely seen as being superior to semi and fully unsupervised methods. However, supervised methods crucially rely upon training sets that need to ...
We propose a new approach to reinforcement learning which combines least squares function approximation with policy iteration. Our method is model-free and completely off policy. ...
We present a new approach for the coarse segmentation of tubular structures in 3D image data. Our algorithm, which requires only few initial values and minimal user interaction, c...
We show that the class of strongly connected graphical models with treewidth at most k can be properly efficiently PAC-learnt with respect to the Kullback-Leibler Divergence. Prev...