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» Robust Regularized Kernel Regression
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PAMI
2010
132views more  PAMI 2010»
13 years 5 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
ICIP
2006
IEEE
14 years 8 months ago
Robust Kernel Regression for Restoration and Reconstruction of Images from Sparse Noisy Data
We introduce a class of robust non-parametric estimation methods which are ideally suited for the reconstruction of signals and images from noise-corrupted or sparsely collected s...
Hiroyuki Takeda, Sina Farsiu, Peyman Milanfar
MA
2010
Springer
93views Communications» more  MA 2010»
13 years 1 months ago
Robustness of reweighted Least Squares Kernel Based Regression
Michiel Debruyne, Andreas Christmann, Mia Hubert, ...
CSDA
2007
128views more  CSDA 2007»
13 years 6 months ago
Regularized linear and kernel redundancy analysis
Redundancy analysis (RA) is a versatile technique used to predict multivariate criterion variables from multivariate predictor variables. The reduced-rank feature of RA captures r...
Yoshio Takane, Heungsun Hwang
SCALESPACE
2005
Springer
14 years 6 days ago
Relations Between Higher Order TV Regularization and Support Vector Regression
We study the connection between higher order total variation (TV) regularization and support vector regression (SVR) with spline kernels in a one-dimensional discrete setting. We p...
Gabriele Steidl, Stephan Didas, Julia Neumann