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PAMI
2011
13 years 2 months ago
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
—Semi-supervised learning concerns the problem of learning in the presence of labeled and unlabeled data. Several boosting algorithms have been extended to semi-supervised learni...
Ke Chen, Shihai Wang
JMLR
2010
145views more  JMLR 2010»
13 years 2 months ago
Parallelizable Sampling of Markov Random Fields
Markov Random Fields (MRFs) are an important class of probabilistic models which are used for density estimation, classification, denoising, and for constructing Deep Belief Netwo...
James Martens, Ilya Sutskever
ASPLOS
2008
ACM
13 years 9 months ago
Hardware counter driven on-the-fly request signatures
Today's processors provide a rich source of statistical information on application execution through hardware counters. In this paper, we explore the utilization of these sta...
Kai Shen, Ming Zhong, Sandhya Dwarkadas, Chuanpeng...
GECCO
2006
Springer
195views Optimization» more  GECCO 2006»
13 years 11 months ago
Studying XCS/BOA learning in Boolean functions: structure encoding and random Boolean functions
Recently, studies with the XCS classifier system on Boolean functions have shown that in certain types of functions simple crossover operators can lead to disruption and, conseque...
Martin V. Butz, Martin Pelikan
AI
2008
Springer
13 years 7 months ago
Speculative plan execution for information gathering
The execution performance of an information gathering plan can suffer significantly due to remote I/O latencies. A streaming dataflow model of execution addresses the problem to s...
Greg Barish, Craig A. Knoblock