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VALUETOOLS
2006
ACM
125views Hardware» more  VALUETOOLS 2006»
13 years 10 months ago
An approximative method for calculating performance measures of Markov processes
We present a new approximation method called value extrapolation for Markov processes with large or infinite state spaces. The method can be applied for calculating any performan...
Juha Leino, Jorma T. Virtamo
ICML
2010
IEEE
13 years 5 months ago
Feature Selection Using Regularization in Approximate Linear Programs for Markov Decision Processes
Approximate dynamic programming has been used successfully in a large variety of domains, but it relies on a small set of provided approximation features to calculate solutions re...
Marek Petrik, Gavin Taylor, Ronald Parr, Shlomo Zi...
AUSAI
2006
Springer
13 years 8 months ago
Efficient AUC Learning Curve Calculation
Abstract. A learning curve of a performance measure provides a graphical method with many benefits for judging classifier properties. The area under the ROC curve (AUC) is a useful...
Remco R. Bouckaert
JMLR
2006
143views more  JMLR 2006»
13 years 4 months ago
Geometric Variance Reduction in Markov Chains: Application to Value Function and Gradient Estimation
We study a sequential variance reduction technique for Monte Carlo estimation of functionals in Markov Chains. The method is based on designing sequential control variates using s...
Rémi Munos
SIGMETRICS
2000
ACM
105views Hardware» more  SIGMETRICS 2000»
13 years 9 months ago
Using the exact state space of a Markov model to compute approximate stationary measures
We present a new approximation algorithm based on an exact representation of the state space S, using decision diagrams, and of the transition rate matrix R, using Kronecker algeb...
Andrew S. Miner, Gianfranco Ciardo, Susanna Donate...