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» Learning for stochastic dynamic programming
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ML
2006
ACM
15 years 2 months ago
Universal parameter optimisation in games based on SPSA
Most game programs have a large number of parameters that are crucial for their performance. While tuning these parameters by hand is rather difficult, efficient and easy to use ge...
Levente Kocsis, Csaba Szepesvári
142
Voted
ICML
2010
IEEE
15 years 3 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...
126
Voted
BMCBI
2006
239views more  BMCBI 2006»
15 years 2 months ago
Applying dynamic Bayesian networks to perturbed gene expression data
Background: A central goal of molecular biology is to understand the regulatory mechanisms of gene transcription and protein synthesis. Because of their solid basis in statistics,...
Norbert Dojer, Anna Gambin, Andrzej Mizera, Bartek...
103
Voted
EOR
2008
123views more  EOR 2008»
15 years 2 months ago
Fixed versus flexible production systems: A real options analysis
In this work, we address investment decisions in production systems by using real options. As is standard in literature, the stochastic variable is assumed to be normally distribu...
Dalila B. M. M. Fontes
109
Voted
UIST
1994
ACM
15 years 6 months ago
Evolutionary Learning of Graph Layout Constraints from Examples
We propose a new evolutionary method of extracting user preferences from examples shown to an automatic graph layout system. Using stochastic methods such as simulated annealing a...
Toshiyuki Masui