There has been much work on applying multiple-instance (MI) learning to contentbased image retrieval (CBIR) where the goal is to rank all images in a known repository using a smal...
We introduce a method for learning Bayesian networks that handles the discretization of continuous variables as an integral part of the learning process. The main ingredient in th...
Many semi-supervised learning algorithms only
deal with binary classification. Their extension to the
multi-class problem is usually obtained by repeatedly
solving a set of bina...
Ranking plays a central role in many Web search and information retrieval applications. Ensemble ranking, sometimes called meta-search, aims to improve the retrieval performance b...
In multi-instance learning, the training set comprises labeled bags that are composed of unlabeled instances, and the task is to predict the labels of unseen bags. Through analyzin...