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» Perspectives on Sparse Bayesian Learning
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16 years 9 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
109
Voted
CVPR
2009
IEEE
15 years 6 months ago
Contextual decomposition of multi-label images
Most research on image decomposition, e.g. image segmentation and image parsing, has predominantly focused on the low-level visual clues within single image and neglected the cont...
Teng Li, Tao Mei, Shuicheng Yan, In-So Kweon, Chil...
CVPR
2004
IEEE
16 years 1 months ago
Representation and Matching of Articulated Shapes
We consider the problem of localizing the articulated and deformable shape of a walking person in a single view. We represent the non-rigid 2D body contour by a Bayesian graphical...
Jiayong Zhang, Robert T. Collins, Yanxi Liu
ICML
2007
IEEE
16 years 14 days ago
Beamforming using the relevance vector machine
Beamformers are spatial filters that pass source signals in particular focused locations while suppressing interference from elsewhere. The widely-used minimum variance adaptive b...
David P. Wipf, Srikantan S. Nagarajan
ECML
2007
Springer
15 years 5 months ago
Principal Component Analysis for Large Scale Problems with Lots of Missing Values
Abstract. Principal component analysis (PCA) is a well-known classical data analysis technique. There are a number of algorithms for solving the problem, some scaling better than o...
Tapani Raiko, Alexander Ilin, Juha Karhunen