51 search results - page 9 / 11 » Exponentiated Gradient Methods for Reinforcement Learning |
NIPS
2008
13 years 9 months ago
2008
In kernel-based regression learning, optimizing each kernel individually is useful when the data density, curvature of regression surfaces (or decision boundaries) or magnitude of...
AI
2009
Springer
14 years 2 months ago
2009
Springer
This paper examines how one can obtain state of the art Chinese word segmentation using global linear models. We provide experimental comparisons that give a detailed road-map for ...
KDD
2010
ACM
13 years 11 months ago
2010
ACM
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
ICML
2005
IEEE
14 years 8 months ago
2005
IEEE
We introduce an expectation maximizationtype (EM) algorithm for maximum likelihood optimization of conditional densities. It is applicable to hidden variable models where the dist...
GECCO
2006
Springer
13 years 11 months ago
2006
Springer
The learning classifier system XCS is an iterative rulelearning system that evolves rule structures based on gradient-based prediction and rule quality estimates. Besides classifi...