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» Motion vector recovery with Gaussian Process Regression
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ICASSP
2011
IEEE
12 years 11 months ago
Improved thresholds for rank minimization
—Nuclear norm minimization (NNM) has recently gained attention for its use in rank minimization problems. In this paper, we define weak, sectional and strong recovery for NNM to...
Samet Oymak, M. Amin Khajehnejad, Babak Hassibi
JMLR
2002
137views more  JMLR 2002»
13 years 7 months ago
The Subspace Information Criterion for Infinite Dimensional Hypothesis Spaces
A central problem in learning is selection of an appropriate model. This is typically done by estimating the unknown generalization errors of a set of models to be selected from a...
Masashi Sugiyama, Klaus-Robert Müller
ECCV
2010
Springer
14 years 1 months ago
Learning Relations Among Movie Characters: A Social Network Perspective
If you have ever watched movies or television shows, you know how easy it is to tell the good characters from the bad ones. Little, however, is known “whether” or “how” com...
NIPS
2007
13 years 9 months ago
Bundle Methods for Machine Learning
We present a globally convergent method for regularized risk minimization problems. Our method applies to Support Vector estimation, regression, Gaussian Processes, and any other ...
Alex J. Smola, S. V. N. Vishwanathan, Quoc V. Le
CVPR
2012
IEEE
11 years 10 months ago
Finite Element based sequential Bayesian Non-Rigid Structure from Motion
Navier’s equations modelling linear elastic solid deformations are embedded within an Extended Kalman Filter (EKF) to compute a sequential Bayesian estimate for the Non-Rigid St...
Antonio Agudo, Begoña Calvo, J. M. M. Monti...