Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in m...
Bo Wang, Jie Tang, Wei Fan, Songcan Chen, Zi Yang,...
The problem of transfer learning, where information gained in one learning task is used to improve performance in another related task, is an important new area of research. While...
Kernel machines have been shown as the state-of-the-art learning techniques for classification. In this paper, we propose a novel general framework of learning the Unified Kernel ...
Abstract. The use of sparse invariant features to recognise classes of actions or objects has become common in the literature. However, features are often "engineered" to...
In this work we try to bridge the gap often encountered by researchers who find themselves with few or no labeled examples from their desired target domain, yet still have access ...