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AMAI
2004
Springer
15 years 8 months 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
101
Voted
UAI
2004
15 years 3 months ago
Discretized Approximations for POMDP with Average Cost
In this paper, we propose a new lower approximation scheme for POMDP with discounted and average cost criterion. The approximating functions are determined by their values at a fi...
Huizhen Yu, Dimitri P. Bertsekas
CEC
2005
IEEE
15 years 8 months ago
XCS with computed prediction for the learning of Boolean functions
Computed prediction represents a major shift in learning classifier system research. XCS with computed prediction, based on linear approximators, has been applied so far to functi...
Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wils...
DSP
2006
15 years 2 months ago
On the application of cross correlation function to subsample discrete time delay estimation
Cross correlation function (CCF) of signals is an important tool of multi-sensors signal processing. Parabola functions are commonly used as parametric models of the CCF in time d...
Lei Zhang 0006, Xiaolin Wu
ADCM
2008
136views more  ADCM 2008»
15 years 2 months ago
Learning and approximation by Gaussians on Riemannian manifolds
Learning function relations or understanding structures of data lying in manifolds embedded in huge dimensional Euclidean spaces is an important topic in learning theory. In this ...
Gui-Bo Ye, Ding-Xuan Zhou