In many machine learning problems, labeled training data is limited but unlabeled data is ample. Some of these problems have instances that can be factored into multiple views, ea...
We propose CCRank, the first parallel algorithm for learning to rank, targeting simultaneous improvement in learning accuracy and efficiency. CCRank is based on cooperative coev...
Shuaiqiang Wang, Byron J. Gao, Ke Wang, Hady Wiraw...
This paper describes a face detection approach via learning local features. The key idea is that local features, being manifested by a collection of pixels in a local region, are ...
Xiangrong Chen, Lie Gu, Stan Z. Li, HongJiang Zhan...
Face representation based on the Visual Codebook becomes popular because of its excellent recognition performance, in which the critical problem is how to learn the most efficien...
We propose a new framework for supervised machine learning. Our goal is to learn from smaller amounts of supervised training data, by collecting a richer kind of training data: an...