Unsupervised one-class learning for automatic outlier removal
Wei Liu, Gang Hua, et al.
CVPR 2014
As Internet information portal become prevalent for both Internet and Intranet, most existing Internet Application Server architectures are not scalable to support large amount of personalization, customization and content adaptation required. In this paper, we propose a framework to capture the information and content dissemination process. Furthermore, we propose a methodology to map this process to a distributed application server environment. By fully exploiting the intersections of user preference at multiple content processing stages, this new framework enables high hit ratio on processing, storage, and transmission of content and thus scales well to support a large number of clients.
Wei Liu, Gang Hua, et al.
CVPR 2014
Chung-Sheng Li, Yuan-Chi Chang, et al.
ISIMP 2001
Chung-Sheng Li, Franklin Fuk-Kay Tong, et al.
Journal of Lightwave Technology
Apostol Paul Natsev, Murray Campbell, et al.
TRECVID 2007