Optimal regression rates for SVMs using Gaussian kernels

@inproceedings{Eberts2012OptimalRR,
  title={Optimal regression rates for SVMs using Gaussian kernels},
  author={Mona Eberts and Ingo Steinwart},
  year={2012}
}
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… CONTINUE READING
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