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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Kernel Affine Projection Algorithms
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Group-sensitive multiple kernel learning for object categorization
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An ACO-based algorithm for parameter optimization of support vector machines
TL;DR: The proposed ACO algorithm is applied on some real world benchmark datasets to validate the feasibility and efficiency, which shows that the new ACO-SVM model can yield promising results.
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Model selection for support vector machines via uniform design
TL;DR: A nested uniform design (UD) methodology is proposed for efficient, robust and automatic model selection for support vector machines (SVMs) and can be treated as a deterministic analog of random search.
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