We propose online decision strategies for time-dependent sequences of linear programs which use no distributional and minimal geometric assumptions about the data. These strategies...
Tatsiana Levina, Yuri Levin, Jeff McGill, Mikhail ...
We argue that in a distributed context, such as the Semantic Web, ontology engineers and data creators often cannot control (or even imagine) the possible uses their data or ontolo...
Gunnar Aastrand Grimnes, Peter Edwards, Alun D. Pr...
An active learner usually assumes there are some labeled data available based on which a moderate classifier is learned and then examines unlabeled data to manually label the mos...
We address instance-based learning from a perceptual organization standpoint and present methods for dimensionality estimation, manifold learning and function approximation. Under...
This paper presents a new learning method for automatic acquisition of translation knowledge from parallel corpora. We apply this learning method to automatic extraction of bilingu...