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» Learning the Structure of Dynamic Probabilistic Networks
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143
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AAAI
2000
15 years 3 months ago
Decision Making under Uncertainty: Operations Research Meets AI (Again)
Models for sequential decision making under uncertainty (e.g., Markov decision processes,or MDPs) have beenstudied in operations research for decades. The recent incorporation of ...
Craig Boutilier
139
Voted
KDD
2009
ACM
203views Data Mining» more  KDD 2009»
16 years 3 months ago
Characterizing individual communication patterns
The increasing availability of electronic communication data, such as that arising from e-mail exchange, presents social and information scientists with new possibilities for char...
R. Dean Malmgren, Jake M. Hofman, Luis A. N. Amara...
RECOMB
2010
Springer
15 years 28 days ago
Predicting Nucleosome Positioning Using Multiple Evidence Tracks
Abstract. We describe a probabilistic model, implemented as a dynamic Bayesian network, that can be used to predict nucleosome positioning along a chromosome based on one or more g...
Sheila M. Reynolds, Zhiping Weng, Jeff A. Bilmes, ...
116
Voted
HT
2005
ACM
15 years 8 months ago
Distributed, real-time computation of community preferences
We describe the integration of smart digital objects with Hebbian learning to create a distributed, real-time, scalable approach to adapting to a community's preferences. We ...
Thomas Lutkenhouse, Michael L. Nelson, Johan Bolle...
107
Voted
RAID
2010
Springer
15 years 28 days ago
An Analysis of Rogue AV Campaigns
Abstract. Rogue antivirus software has recently received extensive attention, justified by the diffusion and efficacy of its propagation. We present a longitudinal analysis of th...
Marco Cova, Corrado Leita, Olivier Thonnard, Angel...