509 search results - page 3 / 102 » Using Learning for Approximation in Stochastic Processes |
ICRA
2007
IEEE
14 years 1 months ago
2007
IEEE
Abstract— The problem of an effective coordination of multiple autonomous robots is one of the most important tasks of the modern robotics. In turn, it is well known that the lea...
QEST
2005
IEEE
14 years 28 days ago
2005
IEEE
Abstract— Approximation techniques for labelled Markov processes on continuous state spaces were developed by Desharnais, Gupta, Jagadeesan and Panangaden. However, it has not be...
ACL
2009
13 years 5 months ago
2009
Stochastic gradient descent (SGD) uses approximate gradients estimated from subsets of the training data and updates the parameters in an online fashion. This learning framework i...
JMLR
2010
13 years 2 months ago
2010
We present an approximate inference approach to parameter estimation in a spatio-temporal stochastic process of the reaction-diffusion type. The continuous space limit of an infer...
JAIR
2010
13 years 5 months ago
2010
Domain-specific features are important in representing problem structure throughout machine learning and decision-theoretic planning. In planning, once state features are provide...