This paper concerns learning and prediction with probabilistic models where the domain sizes of latent variables have no a priori upper-bound. Current approaches represent prior d...
— We present heuristic algorithms for pruning large sets of candidate paths or trajectories down to smaller subsets that maintain desirable characteristics in terms of overall re...
Michael S. Branicky, Ross A. Knepper, James J. Kuf...
In this paper a colour object search algorithm is presented. Given an image, areas of interest are generated (for each database model) by isolating regions whose colours are simil...
Decision trees that are limited to testing a single variable at a node are potentially much larger than trees that allow testing multiple variables at a node. This limitation redu...
We present DL8, an exact algorithm for finding a decision tree that optimizes a ranking function under size, depth, accuracy and leaf constraints. Because the discovery of optimal...