One-class LS-SVM with zero leave-one-out error

Author

Kampmann, Geritt ; Nelles, Oliver

Author_Institution

Dept. of Mech. Eng., Univ. of Siegen, Siegen, Germany

fYear

2014

fDate

9-12 Dec. 2014

Firstpage

1

Lastpage

6

Abstract

This paper extends the closed form calculation of the leave-one-out (LOO) error for least-squares support vector machines (LS-SVMs) from the two-class to the one-class case. Furthermore, it proposes a new algorithm for determining the hyperparameters of a one-class LS-SVM with Gaussian kernels which exploits the efficient LOO error calculation. The standard deviations are selected by prior knowledge while the regularization parameter is optimized in order to obtain a tight decision boundary under the constraint of a zero LOO error.

Keywords

Gaussian processes; optimisation; pattern classification; regression analysis; support vector machines; unsupervised learning; Gaussian kernels; LOO error calculation; closed form calculation; hyperparameter determination; least-squares support vector machines; one-class LS-SVM; one-class classification; regularization parameter optimization; standard deviation selection; tight decision boundary; unsupervised learning task; zero LOO error constraint; zero leave-one-out error; Equations; Kernel; Mathematical model; Optimization; Standards; Support vector machines; Training;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence in Control and Automation (CICA), 2014 IEEE Symposium on

Conference_Location

Orlando, FL

Type

conf

DOI

10.1109/CICA.2014.7013225

Filename

7013225