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» Learning Mixtures of Gaussians
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ICML
2006
IEEE
14 years 9 months ago
Collaborative ordinal regression
Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborati...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
ATAL
2008
Springer
13 years 10 months ago
Sigma point policy iteration
In reinforcement learning, least-squares temporal difference methods (e.g., LSTD and LSPI) are effective, data-efficient techniques for policy evaluation and control with linear v...
Michael H. Bowling, Alborz Geramifard, David Winga...
ICML
2008
IEEE
14 years 9 months ago
Topologically-constrained latent variable models
In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion...
Raquel Urtasun, David J. Fleet, Andreas Geiger, Jo...
CVPR
2006
IEEE
14 years 10 months ago
Image Denoising with Shrinkage and Redundant Representations
Shrinkage is a well known and appealing denoising technique. The use of shrinkage is known to be optimal for Gaussian white noise, provided that the sparsity on the signal's ...
Michael Elad, Boaz Matalon, Michael Zibulevsky
CVPR
2007
IEEE
14 years 10 months ago
Connecting the Out-of-Sample and Pre-Image Problems in Kernel Methods
Kernel methods have been widely studied in the field of pattern recognition. These methods implicitly map, "the kernel trick," the data into a space which is more approp...
Pablo Arias, Gregory Randall, Guillermo Sapiro