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ICML
2006
IEEE
16 years 3 months ago
Bayesian regression with input noise for high dimensional data
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
ICML
2004
IEEE
16 years 3 months ago
Training conditional random fields via gradient tree boosting
Conditional Random Fields (CRFs; Lafferty, McCallum, & Pereira, 2001) provide a flexible and powerful model for learning to assign labels to elements of sequences in such appl...
Thomas G. Dietterich, Adam Ashenfelter, Yaroslav B...
126
Voted
ML
2010
ACM
151views Machine Learning» more  ML 2010»
15 years 1 months ago
Inductive transfer for learning Bayesian networks
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with...
Roger Luis, Luis Enrique Sucar, Eduardo F. Morales
ICNC
2005
Springer
15 years 8 months ago
Novel Leaning Feed-Forward Controller for Accurate Robot Trajectory Tracking
This paper presents a novel learning feed-forward controller design approach for accurate robotics trajectory tracking. Based on the joint nonlinear dynamics characteristics, a mod...
D. Bi, G. L. Wang, Jun Zhang, Q. Xue
114
Voted
COLT
2004
Springer
15 years 8 months ago
Reinforcement Learning for Average Reward Zero-Sum Games
Abstract. We consider Reinforcement Learning for average reward zerosum stochastic games. We present and analyze two algorithms. The first is based on relative Q-learning and the ...
Shie Mannor