In this paper we show the power of sampling techniques in designing efficient distributed algorithms. In particular, we show that using sampling techniques, on some networks, sele...
This paper explores two classes of model adaptation methods for Web search ranking: Model Interpolation and error-driven learning approaches based on a boosting algorithm. The res...
Jianfeng Gao, Qiang Wu, Chris Burges, Krysta Marie...
Background modeling for dynamic scenes is an important problem in the context of real time video surveillance systems. Several nonparametric background models have been proposed t...
Xingzhi Luo, Suchendra M. Bhandarkar, Wei Hua, Hai...
Machine learning methods are often used to classify objects described by hundreds of attributes; in many applications of this kind a great fraction of attributes may be totally irr...
Miron B. Kursa, Aleksander Jankowski, Witold R. Ru...
We study iterative randomized greedy algorithms for generating (elimination) orderings with small induced width and state space size - two parameters known to bound the complexity...
Kalev Kask, Andrew Gelfand, Lars Otten, Rina Decht...