Title :
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
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;
Conference_Titel :
Computational Intelligence in Control and Automation (CICA), 2014 IEEE Symposium on
Conference_Location :
Orlando, FL
DOI :
10.1109/CICA.2014.7013225
