Sample selection bias is a common problem in many real world applications, where training data are obtained under realistic constraints that make them follow a different distribut...
Classifier learning methods commonly assume that the training data consist of randomly drawn examples from the same distribution as the test examples about which the learned model...
Maximum likelihood (ML) estimation is widely used in many computer vision problems involving the estimation of geometric parameters, from conic fitting to bundle adjustment for s...
—Providing independent uniform samples from a system population poses considerable problems in highly dynamic settings, like P2P systems, where the number of participants and the...
Roberto Baldoni, Marco Platania, Leonardo Querzoni...