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» Introduction to Randomized Algorithms
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SIGIR
2009
ACM
15 years 10 months ago
An improved markov random field model for supporting verbose queries
Recent work in supervised learning of term-based retrieval models has shown significantly improved accuracy can often be achieved via better model estimation [2, 10, 11, 17]. In ...
Matthew Lease
167
Voted
GLOBECOM
2008
IEEE
15 years 10 months ago
Throughput Analysis of Randomized Sleep Scheduling with Constrained Connectivity in Wireless Sensor Networks
Abstract—In this paper, we investigate and analyze the expected per-node throughput in a wireless sensor network under a randomized sleep scheduling framework with a connectivity...
Youngsang Kim, Changwoo Yang, Chun-Hung Liu
142
Voted
ICASSP
2008
IEEE
15 years 10 months ago
Maximum conditional likelihood linear regression and maximum a posteriori for hidden conditional random fields speaker adaptatio
This paper shows how to improve Hidden Conditional Random Fields (HCRFs) for phone classification by applying various speaker adaptation techniques. These include Maximum A Poste...
Yun-Hsuan Sung, Constantinos Boulis, Daniel Jurafs...
ICPR
2008
IEEE
15 years 9 months ago
Face super-resolution using 8-connected Markov Random Fields with embedded prior
In patch based face super-resolution method, the patch size is usually very small, and neighbor patches’ relationship via overlapped regions is only to keep smoothness of recons...
Kai Guo, Xiaokang Yang, Rui Zhang, Guangtao Zhai, ...
118
Voted
DSN
2007
IEEE
15 years 9 months ago
R-Sentry: Providing Continuous Sensor Services against Random Node Failures
The success of sensor-driven applications is reliant on whether a steady stream of data can be provided by the underlying system. This need, however, poses great challenges to sen...
Shengchao Yu, Yanyong Zhang