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IJCNN
2006
IEEE

Learning the Kernel in Mahalanobis One-Class Support Vector Machines

12 years 22 days 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 as a convex combination of base kernels, we show that the weighting coefficients can be learned via quadratically constrained quadratic programming (QCQP) or second order cone programming (SOCP) methods. Performance on both toy and real-world data sets show promising results. This paper thus offers another demonstration of the synergy between convex optimization and kernel methods.
Ivor W. Tsang, James T. Kwok, Shutao Li
Added 11 Jun 2010
Updated 11 Jun 2010
Type Conference
Year 2006
Where IJCNN
Authors Ivor W. Tsang, James T. Kwok, Shutao Li
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