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» Computing regularization paths for learning multiple kernels
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PAMI
2010
132views more  PAMI 2010»
13 years 6 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
ICCV
2009
IEEE
15 years 16 days ago
Multiple Kernels for Object Detection
Our objective is to obtain a state-of-the art object category detector by employing a state-of-the-art image classifier to search for the object in all possible image subwindows....
Andrea Vedaldi, Varun Gulshan, Manik Varma, Andrew...
ICDM
2006
IEEE
146views Data Mining» more  ICDM 2006»
14 years 1 months ago
Boosting Kernel Models for Regression
This paper proposes a general boosting framework for combining multiple kernel models in the context of both classification and regression problems. Our main approach is built on...
Ping Sun, Xin Yao
JMLR
2010
147views more  JMLR 2010»
13 years 2 months ago
Spectral Regularization Algorithms for Learning Large Incomplete Matrices
We use convex relaxation techniques to provide a sequence of regularized low-rank solutions for large-scale matrix completion problems. Using the nuclear norm as a regularizer, we...
Rahul Mazumder, Trevor Hastie, Robert Tibshirani
ECCV
2010
Springer
13 years 10 months ago
Bilinear Kernel Reduced Rank Regression for Facial Expression Synthesis
In the last few years, Facial Expression Synthesis (FES) has been a flourishing area of research driven by applications in character animation, computer games, and human computer ...