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One-class LS-SVM with zero leave-one-out error | IEEE Conference Publication | IEEE Xplore

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


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 ca...Show More

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.
Date of Conference: 09-12 December 2014
Date Added to IEEE Xplore: 19 January 2015
Electronic ISBN:978-1-4799-4530-6

ISSN Information:

Conference Location: Orlando, FL, USA

References

References is not available for this document.