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CDC
2009
IEEE
180views Control Systems» more  CDC 2009»
9 years 1 months ago
Robustness analysis for Least Squares kernel based regression: an optimization approach
—In kernel based regression techniques (such as Support Vector Machines or Least Squares Support Vector Machines) it is hard to analyze the influence of perturbed inputs on the ...
Tillmann Falck, Johan A. K. Suykens, Bart De Moor
MA
2010
Springer
93views Communications» more  MA 2010»
8 years 4 months ago
Robustness of reweighted Least Squares Kernel Based Regression
Michiel Debruyne, Andreas Christmann, Mia Hubert, ...
FOCM
2006
97views more  FOCM 2006»
8 years 10 months ago
Learning Rates of Least-Square Regularized Regression
This paper considers the regularized learning algorithm associated with the leastsquare loss and reproducing kernel Hilbert spaces. The target is the error analysis for the regres...
Qiang Wu, Yiming Ying, Ding-Xuan Zhou
ECAI
2006
Springer
9 years 1 months ago
Least Squares SVM for Least Squares TD Learning
Abstract. We formulate the problem of least squares temporal difference learning (LSTD) in the framework of least squares SVM (LS-SVM). To cope with the large amount (and possible ...
Tobias Jung, Daniel Polani
IDEAL
2004
Springer
9 years 3 months ago
Orthogonal Least Square with Boosting for Regression
A novel technique is presented to construct sparse regression models based on the orthogonal least square method with boosting. This technique tunes the mean vector and diagonal c...
Sheng Chen, Xunxian Wang, David J. Brown
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