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PAMI
2011
13 years 2 months ago
Greedy Learning of Binary Latent Trees
—Inferring latent structures from observations helps to model and possibly also understand underlying data generating processes. A rich class of latent structures are the latent ...
Stefan Harmeling, Christopher K. I. Williams
AI
1998
Springer
13 years 7 months ago
Model-Based Average Reward Reinforcement Learning
Reinforcement Learning (RL) is the study of programs that improve their performance by receiving rewards and punishments from the environment. Most RL methods optimize the discoun...
Prasad Tadepalli, DoKyeong Ok
JAIR
2002
122views more  JAIR 2002»
13 years 7 months ago
Competitive Safety Analysis: Robust Decision-Making in Multi-Agent Systems
Much work in AI deals with the selection of proper actions in a given (known or unknown) environment. However, the way to select a proper action when facing other agents is quite ...
Moshe Tennenholtz
BMCBI
2011
13 years 2 months ago
The impact of quantitative optimization of hybridization conditions on gene expression analysis
Background: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studie...
Peter Sykacek, David P. Kreil, Lisa A. Meadows, Ri...
ICNP
2007
IEEE
14 years 1 months ago
Adaptive Optimization of Rate Adaptation Algorithms in Multi-Rate WLANs
— Rate adaptation is one of the basic functionalities in today’s 802.11 wireless LANs (WLANs). Although it is primarily designed to cope with the variability of wireless channe...
Jaehyuk Choi, Jongkeun Na, Kihong Park, Chong-kwon...