424 search results - page 2 / 85 » Boosted sampling: approximation algorithms for stochastic op... |
FOCS
2005
IEEE
13 years 11 months ago
2005
IEEE
Stochastic optimization problems provide a means to model uncertainty in the input data where the uncertainty is modeled by a probability distribution over the possible realizatio...
SIAMJO
2002
13 years 5 months ago
2002
In this paper we study a Monte Carlo simulation based approach to stochastic discrete optimization problems. The basic idea of such methods is that a random sample is generated and...
PKDD
2009
Springer
13 years 10 months ago
2009
Springer
Abstract. This paper focuses on Active Learning with a limited number of queries; in application domains such as Numerical Engineering, the size of the training set might be limite...
ANOR
2010
13 years 3 months ago
2010
This paper considers online stochastic optimization problems where uncertainties are characterized by a distribution that can be sampled and where time constraints severely limit t...
ML
2002
ACM
13 years 5 months ago
2002
ACM
An issue that is critical for the application of Markov decision processes MDPs to realistic problems is how the complexity of planning scales with the size of the MDP. In stochas...