In this paper, we propose a novel method to select the most informative subset of features, which has little redundancy and very strong discriminating power. Our proposed approach...
Si Liu, Hairong Liu, Longin Jan Latecki, Shuicheng...
Research in reinforcement learning has produced algorithms for optimal decision making under uncertainty that fall within two main types. The first employs a Bayesian framework, ...
—We propose an efficient and robust solution for image set classification. A joint representation of an image set is proposed which includes the image samples of the set and thei...
— In the past, dynamic voltage and frequency scaling (DVFS) has been widely used for power and energy optimization in embedded system design. As thermal issues become increasingl...
Yongpan Liu, Huazhong Yang, Robert P. Dick, Hui Wa...
This article aims at making iterative optimization practical and usable by speeding up the evaluation of a large range of optimizations. Instead of using a full run to evaluate a s...
Grigori Fursin, Albert Cohen, Michael F. P. O'Boyl...