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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Clinician's road map to wavelet EEG as an Alzheimer's disease biomarker.
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Feature selection by transfer learning with linear regularized models
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Hybrid model based on Genetic Algorithms and SVM applied to variable selection within fruit juice classification.
Carlos Fernandez-Lozano,C. Canto,Marcos Gestal,José Manuel Andrade-Garda,Juan R. Rabuñal,Julián Dorado,Alejandro Pazos +6 more
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Tree-Guided Sparse Coding for Brain Disease Classification
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References
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Todd R. Golub,Todd R. Golub,Donna K. Slonim,Pablo Tamayo,Christine Huard,Michelle Gaasenbeek,Jill P. Mesirov,Hilary A. Coller,Mignon L. Loh,James R. Downing,Michael A. Caligiuri,Clara D. Bloomfield,Eric S. Lander +12 more
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