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» Kernelization for Convex Recoloring
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IJCNN
2006
IEEE
14 years 1 months ago
Learning the Kernel in Mahalanobis One-Class Support Vector Machines
— In this paper, we show that one-class SVMs can also utilize data covariance in a robust manner to improve performance. Furthermore, by constraining the desired kernel function ...
Ivor W. Tsang, James T. Kwok, Shutao Li
ICASSP
2011
IEEE
12 years 11 months ago
Subspace pursuit method for kernel-log-linear models
This paper presents a novel method for reducing the dimensionality of kernel spaces. Recently, to maintain the convexity of training, loglinear models without mixtures have been u...
Yotaro Kubo, Simon Wiesler, Ralf Schlüter, He...
CDC
2009
IEEE
180views Control Systems» more  CDC 2009»
13 years 10 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
ICML
2008
IEEE
14 years 8 months ago
Composite kernel learning
The Support Vector Machine (SVM) is an acknowledged powerful tool for building classifiers, but it lacks flexibility, in the sense that the kernel is chosen prior to learning. Mul...
Marie Szafranski, Yves Grandvalet, Alain Rakotomam...
ICML
2004
IEEE
14 years 8 months ago
Multiple kernel learning, conic duality, and the SMO algorithm
While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. ...
Francis R. Bach, Gert R. G. Lanckriet, Michael I. ...