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JMLR
2010
135views more  JMLR 2010»
13 years 2 months ago
Finite-sample Analysis of Bellman Residual Minimization
We consider the Bellman residual minimization approach for solving discounted Markov decision problems, where we assume that a generative model of the dynamics and rewards is avai...
Odalric-Ambrym Maillard, Rémi Munos, Alessa...
ML
2002
ACM
178views Machine Learning» more  ML 2002»
13 years 7 months ago
Metric-Based Methods for Adaptive Model Selection and Regularization
We present a general approach to model selection and regularization that exploits unlabeled data to adaptively control hypothesis complexity in supervised learning tasks. The idea ...
Dale Schuurmans, Finnegan Southey
BMCBI
2007
156views more  BMCBI 2007»
13 years 7 months ago
Accuracy of structure-based sequence alignment of automatic methods
Background: Accurate sequence alignments are essential for homology searches and for building three-dimensional structural models of proteins. Since structure is better conserved ...
Changhoon Kim, Byungkook Lee
ISBI
2008
IEEE
14 years 8 months ago
Landmark selection for shape model construction via equalization of variance
Model-based segmentation approaches, such as those employing Active Shape Models (ASMs), have proved to be useful for medical image segmentation and understanding. To build the mo...
Sylvia Rueda, Jayaram K. Udupa, Li Bai
EMSOFT
2008
Springer
13 years 9 months ago
Automatically transforming and relating Uppaal models of embedded systems
Relations between models are important for effective automatic validation, for comparing implementations with specifications, and for increased understanding of embedded systems d...
Timothy Bourke, Arcot Sowmya