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CP
1999
Springer
13 years 9 months ago
An Interval Constraint Approach to Handle Parametric Ordinary Differential Equations for Decision Support
The behaviour of many systems is naturally modelled by a set of ordinary differential equations (ODEs) which are parametric. Since decisions are often based on relations over these...
Jorge Cruz, Pedro Barahona
CVPR
2004
IEEE
14 years 7 months ago
Propagation Networks for Recognition of Partially Ordered Sequential Action
We present Propagation Networks (P-Nets), a novel approach for representing and recognizing sequential activities that include parallel streams of action. We represent each activi...
Yifan Shi, Yan Huang, David Minnen, Aaron F. Bobic...
ECML
2006
Springer
13 years 7 months ago
Reinforcement Learning for MDPs with Constraints
In this article, I will consider Markov Decision Processes with two criteria, each defined as the expected value of an infinite horizon cumulative return. The second criterion is e...
Peter Geibel
CPAIOR
2009
Springer
14 years 2 days ago
Learning How to Propagate Using Random Probing
Abstract. In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usu...
Efstathios Stamatatos, Kostas Stergiou
NN
2006
Springer
163views Neural Networks» more  NN 2006»
13 years 5 months ago
Machine learning approaches for estimation of prediction interval for the model output
A novel method for estimating prediction uncertainty using machine learning techniques is presented. Uncertainty is expressed in the form of the two quantiles (constituting the pr...
Durga L. Shrestha, Dimitri P. Solomatine