We analyze the distribution of margins of the training examples with respect to the generated voting classification rule, where the margin of an example is simply the difference between the number of correct votes and the maximum number of votes received by any incorrect label.Expand

We have developed a new point-based POMDP algorithm that exploits the notion of optimally reachable belief spaces to improve com- putational efficiency.Expand

We investigate the following problem: Given a set of documents of a particular topic or class P , and a large set M of mixed documents, identify the documents from class P in M .Expand

POMDPs provide a principled framework for planning under uncertainty, but are computationally intractable, due to the "Curse of dimensionality" and the "curse of history".Expand

We use a factored model to represent separately the fully and partially observable components of a robot’s state and derive a compact lower-dimensional representation of its belief space.Expand

We transform the problem into a problem of learning with noise by labeling all unlabeled examples as negative and use a linear function to learn from the noisy examples.Expand