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Neural Processing Letters
Erschienen in: Neural Processing Letters 1/2016

01.02.2016

Enhancing Least Square Support Vector Regression with Gradient Information

verfasst von: Xiao Jian Zhou, Ting Jiang

Erschienen in: Neural Processing Letters | Ausgabe 1/2016

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Abstract

Traditional methods of constructing of least square support vector regression (LSSVR) do not consider the gradients of the true function but just think about the exact responses at samples. If gradient information is easy to get, it should be used to enhance the surrogate. In this paper, the gradient-enhanced least square support vector regression (GELSSVR) is developed with a direct formulation by incorporating gradient information into the traditional LSSVR. The efficiencies of this technique are compared by analytical function fitting and two real life problems (the recent U.S. actuarial life table and Borehole). The results show that GELSSVR provides more reliable prediction results than LSSVR alone.
Vorheriger Artikel Finite-Time Anti-synchronization Control of Memristive Neural Networks With Stochastic Perturbations
Nächster Artikel Kernel Local Sparse Representation Based Classifier

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Metadaten
Titel
Enhancing Least Square Support Vector Regression with Gradient Information
verfasst von
Xiao Jian Zhou
Ting Jiang
Publikationsdatum
01.02.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2016
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-014-9402-5

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