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» Generalization Bounds for Learning Kernels
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ICML
2007
IEEE
14 years 10 months ago
Multifactor Gaussian process models for style-content separation
We introduce models for density estimation with multiple, hidden, continuous factors. In particular, we propose a generalization of multilinear models using nonlinear basis functi...
Jack M. Wang, David J. Fleet, Aaron Hertzmann
NIPS
2007
13 years 11 months ago
Bayesian Co-Training
We propose a Bayesian undirected graphical model for co-training, or more generally for semi-supervised multi-view learning. This makes explicit the previously unstated assumption...
Shipeng Yu, Balaji Krishnapuram, Rómer Rosa...
PAMI
2010
337views more  PAMI 2010»
13 years 8 months ago
Single-Image Super-Resolution Using Sparse Regression and Natural Image Prior
—This paper proposes a framework for single-image super-resolution. The underlying idea is to learn a map from input low-resolution images to target high-resolution images based ...
Kwang In Kim, Younghee Kwon
COLT
2004
Springer
14 years 3 months ago
Sparseness Versus Estimating Conditional Probabilities: Some Asymptotic Results
One of the nice properties of kernel classifiers such as SVMs is that they often produce sparse solutions. However, the decision functions of these classifiers cannot always be u...
Peter L. Bartlett, Ambuj Tewari
ICALP
2004
Springer
14 years 3 months ago
Learning a Hidden Subgraph
We consider the problem of learning a labeled graph from a given family of graphs on n vertices in a model where the only allowed operation is to query whether a set of vertices i...
Noga Alon, Vera Asodi