LS-SVM Hyperparameter Selection with a Nonparametric Noise Estimator

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.

• Type: Conference
• Year: 2005
• Where: ICANN
• Authors: Amaury Lendasse, Yongnan Ji, Nima Reyhani, Michel Verleysen
• Keywords: Gaussian Kernels, ICANN 2005, Nonparametric Noise Estimator, Regularization Parameter λ