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AMAI
2004
Springer
14 years 29 days ago
Approximate Probabilistic Constraints and Risk-Sensitive Optimization Criteria in Markov Decision Processes
The majority of the work in the area of Markov decision processes has focused on expected values of rewards in the objective function and expected costs in the constraints. Althou...
Dmitri A. Dolgov, Edmund H. Durfee
TIT
1998
126views more  TIT 1998»
13 years 7 months ago
An Asymptotic Property of Model Selection Criteria
—Probability models are estimated by use of penalized log-likelihood criteria related to AIC and MDL. The accuracies of the density estimators are shown to be related to the trad...
Yuhong Yang, Andrew R. Barron
TASLP
2008
105views more  TASLP 2008»
13 years 7 months ago
Optimizing the Performance of Spoken Language Recognition With Discriminative Training
The performance of spoken language recognition system is typically formulated to reflect the detection cost and the strategic decision points along the detection-error-tradeoff cur...
Donglai Zhu, Haizhou Li, Bin Ma, Chin-Hui Lee
BMCBI
2010
156views more  BMCBI 2010»
13 years 7 months ago
Mathematical model for empirically optimizing large scale production of soluble protein domains
Background: Efficient dissection of large proteins into their structural domains is critical for high throughput proteome analysis. So far, no study has focused on mathematically ...
Eisuke Chikayama, Atsushi Kurotani, Takanori Tanak...
RSS
2007
176views Robotics» more  RSS 2007»
13 years 9 months ago
Active Policy Learning for Robot Planning and Exploration under Uncertainty
Abstract— This paper proposes a simulation-based active policy learning algorithm for finite-horizon, partially-observed sequential decision processes. The algorithm is tested i...
Ruben Martinez-Cantin, Nando de Freitas, Arnaud Do...