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» On the Noise Model of Support Vector Machines Regression
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PAMI
2010
132views more  PAMI 2010»
13 years 7 months ago
Maximum Likelihood Model Selection for 1-Norm Soft Margin SVMs with Multiple Parameters
—Adapting the hyperparameters of support vector machines (SVMs) is a challenging model selection problem, especially when flexible kernels are to be adapted and data are scarce....
Tobias Glasmachers, Christian Igel
ESANN
2004
13 years 10 months ago
Sparse LS-SVMs using additive regularization with a penalized validation criterion
This paper is based on a new way for determining the regularization trade-off in least squares support vector machines (LS-SVMs) via a mechanism of additive regularization which ha...
Kristiaan Pelckmans, Johan A. K. Suykens, Bart De ...
ICML
2005
IEEE
14 years 9 months ago
Supervised versus multiple instance learning: an empirical comparison
We empirically study the relationship between supervised and multiple instance (MI) learning. Algorithms to learn various concepts have been adapted to the MI representation. Howe...
Soumya Ray, Mark Craven
CSDA
2010
133views more  CSDA 2010»
13 years 6 months ago
Optimized fixed-size kernel models for large data sets
A modified active subset selection method based on quadratic R
Kris De Brabanter, Jos De Brabanter, Johan A. K. S...
KDD
2006
ACM
165views Data Mining» more  KDD 2006»
14 years 9 months ago
Training linear SVMs in linear time
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like t...
Thorsten Joachims