We consider the general problem of learning from both labeled and unlabeled data. Given a set of data points, only a few of them are labeled, and the remaining points are unlabele...
Fei Wang, Changshui Zhang, Helen C. Shen, Jingdong...
Label ranking studies the problem of learning a mapping from instances to rankings over a predefined set of labels. Hitherto existing approaches to label ranking implicitly operat...
A label consistent K-SVD (LC-KSVD) algorithm to learn a discriminative dictionary for sparse coding is presented. In addition to using class labels of training data, we also assoc...
Applications that adapt to a particular end user often make inaccurate predictions during the early stages when training data is limited. Although an end user can improve the lear...
Weng-Keen Wong, Ian Oberst, Shubhomoy Das, Travis ...
This paper presents a novel framework for applying semantic labels to events within a track. A track is a two-dimensional (2D) or a three-dimensional (3D) signal in time where eac...