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Article Dans Une Revue Pattern Recognition Letters Année : 2011

Learning general Gaussian kernel hyperparameters of SVMs using optimization on symmetric positive-definite matrices manifold

Résumé

We propose a new method for general Gaussian kernel hyperparameter optimization for support vector machines classification. The hyperparameters are constrained to lie on a differentiable manifold. The proposed optimization technique is based on a gradient-like descent algorithm adapted to the geometrical structure of the manifold of symmetric positive-definite matrices. We compare the performance of our approach with the classical support vector machine for classification and with other methods of the state of the art on toy data and on real world data sets.

Dates et versions

hal-00639117 , version 1 (08-11-2011)

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Citer

Fahed Abdallah, Hicham Laanaya, Hichem Snoussi, Cédric Richard. Learning general Gaussian kernel hyperparameters of SVMs using optimization on symmetric positive-definite matrices manifold. Pattern Recognition Letters, 2011, 32 (13), pp.1511-1515. ⟨10.1016/j.patrec.2011.05.009⟩. ⟨hal-00639117⟩
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