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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Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm
Eiman Tamah Al-Shammari,Afram Keivani,Shahaboddin Shamshirband,Ali Mostafaeipour,Por Lip Yee,Dalibor Petković,Sudheer Ch +6 more
TL;DR: The experimental results show that the developed SVM-FFA models can be used with certainty for further work on formulating novel model predictive strategies in district heating systems.
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Deep Fisher Kernels -- End to End Learning of the Fisher Kernel GMM Parameters
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Gene selection algorithms for microarray data based on least squares support vector machine.
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Proceedings ArticleDOI
Efficient parameter selection for support vector machines in classification and regression via model-based global optimization
Holger Fröhlich,Andreas Zell +1 more
TL;DR: In this paper, an online Gaussian process model of the error surface in parameter space and sampling systematically at points for which the so-called expected improvement is highest was proposed to find good SVM parameters.
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