—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Many learning tasks for computer vision problems can be described by multiple views or multiple features. These views can be exploited in order to learn from unlabeled data, a.k.a....
We investigate improvements of AdaBoost that can exploit the fact that the weak hypotheses are one-sided, i.e. either all its positive (or negative) predictions are correct. In pa...
In TREC 2003, our experiments have been concentrated only on the topic distillation task. We first simply apply the term-based technique to the .GOV web collection, and then re-r...
Distance metric is widely used in similarity estimation. In this paper we find that the most popular Euclidean and Manhattan distance may not be suitable for all data distribution...