509 search results - page 21 / 102 » Using Learning for Approximation in Stochastic Processes |
NIPS
2007
13 years 8 months ago
2007
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...
IWANN
1999
Springer
13 years 11 months ago
1999
Springer
To avoid the curse of dimensionality, function approximators are used in reinforcement learning to learn value functions for individual states. In order to make better use of comp...
NIPS
2001
13 years 8 months ago
2001
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
Stationarity is often an unrealistic prior assumption for Gaussian process regression. One solution is to predefine an explicit nonstationary covariance function, but such covaria...
ISMB
1994
13 years 8 months ago
1994
We describe and demonstrate the effectiveness of a method of predicting protein secondary structures, sheet regions in particular, using a class of stochastic tree grammars as rep...