Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...
In this paper, we develop a geometric framework for linear or nonlinear discriminant subspace learning and classification. In our framework, the structures of classes are conceptu...
In recent years, active learning methods based on experimental design achieve state-of-the-art performance in text classification applications. Although these methods can exploit ...
The use of unlabeled data to aid classification is important as labeled data is often available in limited quantity. Instead of utilizing training samples directly into semi-super...
In this paper, we address the problem of learning when some cases are fully labeled while other cases are only partially labeled, in the form of partial labels. Partial labels are...