Choosing Multiple Parameters for Support Vector Machines
TLDR
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters.Abstract:
The problem of automatically tuning multiple parameters for pattern recognition Support Vector Machines (SVMs) is considered. This is done by minimizing some estimates of the generalization error of SVMs using a gradient descent algorithm over the set of parameters. Usual methods for choosing parameters, based on exhaustive search become intractable as soon as the number of parameters exceeds two. Some experimental results assess the feasibility of our approach for a large number of parameters (more than 100) and demonstrate an improvement of generalization performance.read more
Citations
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Evolutionary tuning of multiple SVM parameters
Frauke Friedrichs,Christian Igel +1 more
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l p -Norm Multiple Kernel Learning
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Toward an Optimal SVM Classification System for Hyperspectral Remote Sensing Images
Yakoub Bazi,Farid Melgani +1 more
TL;DR: This paper proposes a classification system based on a genetic optimization framework formulated in such a way as to detect the best discriminative features without requiring the a priori setting of their number by the user and to estimate the best SVM parameters in a completely automatic way.
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Efficient leave-one-out cross-validation of kernel Fisher discriminant classifiers
TL;DR: It is shown that leave-one-out cross-validation of kernel Fisher discriminant classifiers can be implemented with a computational complexity of only O (l 3 ) operations rather than the O ( l 4 ) of a naive implementation, where l is the number of training patterns.
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