Electronic Journal of Statistics

Optimal regression rates for SVMs using Gaussian kernels

Mona Eberts and Ingo Steinwart

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Support vector machines (SVMs) using Gaussian kernels are one of the standard and state-of-the-art learning algorithms. In this work, we establish new oracle inequalities for such SVMs when applied to either least squares or conditional quantile regression. With the help of these oracle inequalities we then derive learning rates that are (essentially) minmax optimal under standard smoothness assumptions on the target function. We further utilize the oracle inequalities to show that these learning rates can be adaptively achieved by a simple data-dependent parameter selection method that splits the data set into a training and a validation set.

Article information

Electron. J. Statist., Volume 7 (2013), 1-42.

First available in Project Euclid: 11 January 2013

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Zentralblatt MATH identifier

Primary: 62G08: Nonparametric regression
Secondary: 62G05: Estimation 68Q32: Computational learning theory [See also 68T05] 68T05: Learning and adaptive systems [See also 68Q32, 91E40]

Least squares regression quantile estimation support vector machines


Eberts, Mona; Steinwart, Ingo. Optimal regression rates for SVMs using Gaussian kernels. Electron. J. Statist. 7 (2013), 1--42. doi:10.1214/12-EJS760. https://projecteuclid.org/euclid.ejs/1357913280

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