Ls-Svm Hyperparameter Selection with a Nonparametric Noise Estimator
Abstract
This Paper Presents a New Method for the Selection of the Two Hyperparameters of Least Squares Support Vector Machine (Ls-Svm) Approximators with Gaussian Kernels. the Two Hyperparameters Are the Width Σ of the Gaussian Kernels and the Regularization Parameter Λ. for Different Values of Σ, a Nonparametric Noise Estimator (Nne) is Introduced to Estimate the Variance of the Noise on the Outputs. the Nne Allows the Determination of the Best Λ for Each Given Σ. a Leave-One-Out Methodology is Then Applied to Select the Best Σ. Therefore, This Method Transforms the Double Optimization Problem into a Single Optimization One. the Method is Tested on 2 Problems: A Toy Example and the Pumadyn Regression Benchmark. © Springer-Verlag Berlin Heidelberg 2005.
Recommended Citation
A. Lendasse et al., "Ls-Svm Hyperparameter Selection with a Nonparametric Noise Estimator," Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3697 LNCS, pp. 625 - 630, Springer, Dec 2005.
Department(s)
Engineering Management and Systems Engineering
Keywords and Phrases
Least Squares Support Vector Machines; Leave-one-out; Noise Estimation; Regression
International Standard Book Number (ISBN)
978-354028755-1
International Standard Serial Number (ISSN)
1611-3349; 0302-9743
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Springer, All rights reserved.
Publication Date
01 Dec 2005
