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ML
2002
ACM
168views Machine Learning» more  ML 2002»
13 years 9 months ago
On Average Versus Discounted Reward Temporal-Difference Learning
We provide an analytical comparison between discounted and average reward temporal-difference (TD) learning with linearly parameterized approximations. We first consider the asympt...
John N. Tsitsiklis, Benjamin Van Roy
ESANN
2007
13 years 11 months ago
Complexity bounds of radial basis functions and multi-objective learning
In the paper, the problem of multi-objective (MOBJ) learning is discussed. The problem of obtaining apparent (effective) complexity measure, which is one of the objectives, is con...
Illya Kokshenev, Antônio de Pádua Bra...
CRV
2009
IEEE
115views Robotics» more  CRV 2009»
14 years 4 months ago
Learning Model Complexity in an Online Environment
In this paper we introduce the concept and method for adaptively tuning the model complexity in an online manner as more examples become available. Challenging classification pro...
Dan Levi, Shimon Ullman
JMLR
2010
143views more  JMLR 2010»
13 years 4 months ago
Rademacher Complexities and Bounding the Excess Risk in Active Learning
Sequential algorithms of active learning based on the estimation of the level sets of the empirical risk are discussed in the paper. Localized Rademacher complexities are used in ...
Vladimir Koltchinskii
ICIP
2009
IEEE
14 years 11 months ago
Learning Large Margin Likelihoods For Realtime Head Pose Tracking
We consider the problem of head tracking and pose estimation in realtime from low resolution images. Tracking and pose recognition are treated as two coupled problems in a probabi...