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» Learning with Global Cost in Stochastic Environments
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COLT
2008
Springer
13 years 7 months ago
Adapting to a Changing Environment: the Brownian Restless Bandits
In the multi-armed bandit (MAB) problem there are k distributions associated with the rewards of playing each of k strategies (slot machine arms). The reward distributions are ini...
Aleksandrs Slivkins, Eli Upfal
ICC
2007
IEEE
120views Communications» more  ICC 2007»
13 years 11 months ago
Dynamic Network Selection using Kernels
—We present a new algorithm for vertical handover and dynamic network selection, based on a combination of multiattribute utility theory, kernel learning and stochastic gradient ...
Eric van den Berg, Praveen Gopalakrishnan, Byungsu...
IJCAI
1997
13 years 6 months ago
Learning Topological Maps with Weak Local Odometric Information
cal maps provide a useful abstraction for robotic navigation and planning. Although stochastic mapscan theoreticallybe learned using the Baum-Welch algorithm,without strong prior ...
Hagit Shatkay, Leslie Pack Kaelbling
NIPS
2007
13 years 6 months ago
Anytime Induction of Cost-sensitive Trees
Machine learning techniques are increasingly being used to produce a wide-range of classifiers for complex real-world applications that involve nonuniform testing costs and miscl...
Saher Esmeir, Shaul Markovitch
COR
2007
86views more  COR 2007»
13 years 5 months ago
Sourcing with random yields and stochastic demand: A newsvendor approach
We studied a supplier selection problem, where a buyer, while facing random demand, is to decide ordering quantities from a set of suppliers with different yields and prices.We pr...
Shitao Yang, Jian Yang, Layek Abdel-Malek