Classification algorithms typically induce population-wide models that are trained to perform well on average on expected future instances. We introduce a Bayesian framework for l...
Finding a small set of representative instances for large datasets can bring various benefits to data mining practitioners so they can (1) build a learner superior to the one cons...
An efficient adaptive multigrid level set method for front propagation purposes in three dimensional medical image processing and segmentation is presented. It is able to deal with...
Marc Droske, Bernhard Meyer, Martin Rumpf, Carlo S...
Online reviews provide consumers with valuable information that guides their decisions on a variety of fronts: from entertainment and shopping to medical services. Although the pr...
Abstract. We deal with two important problems in pattern recognition that arise in the analysis of large datasets. While most feature subset selection methods use statistical techn...