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JMLR
2006
124views more  JMLR 2006»
15 years 13 days ago
A Direct Method for Building Sparse Kernel Learning Algorithms
Many kernel learning algorithms, including support vector machines, result in a kernel machine, such as a kernel classifier, whose key component is a weight vector in a feature sp...
Mingrui Wu, Bernhard Schölkopf, Gökhan H...
ICML
2007
IEEE
16 years 1 months ago
Bayesian compressive sensing and projection optimization
This paper introduces a new problem for which machine-learning tools may make an impact. The problem considered is termed "compressive sensing", in which a real signal o...
Shihao Ji, Lawrence Carin
111
Voted
ICML
2009
IEEE
16 years 1 months ago
Group lasso with overlap and graph lasso
We propose a new penalty function which, when used as regularization for empirical risk minimization procedures, leads to sparse estimators. The support of the sparse vector is ty...
Laurent Jacob, Guillaume Obozinski, Jean-Philippe ...
ICML
2007
IEEE
16 years 1 months ago
Sparse probabilistic classifiers
The scores returned by support vector machines are often used as a confidence measures in the classification of new examples. However, there is no theoretical argument sustaining ...
Romain Hérault, Yves Grandvalet
82
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ALT
2008
Springer
15 years 9 months ago
Generalization Bounds for K-Dimensional Coding Schemes in Hilbert Spaces
We give a bound on the expected reconstruction error for a general coding method where data in a Hilbert space are represented by finite dimensional coding vectors. The result can...
Andreas Maurer, Massimiliano Pontil