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 ...
Given its importance, the problem of predicting rare classes in large-scale multi-labeled data sets has attracted great attentions in the literature. However, the rare-class probl...
Structured peer-to-peer (P2P) overlays have been successfully employed in many applications to locate content. However, they have been less effective in handling massive amounts o...
Support vector machines (SVMs) have been widely used in multimedia retrieval to learn a concept in order to find the best matches. In such a SVM active learning environment, the ...
Scheduling for speculative parallelization is a problem that remained unsolved despite its importance. Simple methods such as Fixed-Size Chunking (FSC) need several ‘dry-runs’...