# Linear dependency between epsilon and the input noise in
epsilon-support vector regression

### James T. Kwok

**Abstract:**
In using the epsilon-support vector regression (epsilon-SVR) algorithm, one has to
decide on a suitable value of the insensitivity parameter epsilon.
Smola et al determined its ``optimal'' choice based on
maximizing the statistical efficiency of a location parameter estimator.
While they successfully predicted a linear scaling
between the optimal
epsilon and the noise in the data,
the value of the theoretically optimal
epsilon does not have a close match with its experimentally observed
counterpart.
In this paper, we attempt to better explain the experimental results there,
by
analyzing a toy problem with a closer setting to the epsilon-SVR.
Our resultant predicted choice of epsilon is much closer to the
experimentally observed value, while still demonstrating
a linear trend with
the data noise.
*Proceedings of the International Conference on Artificial
Neural Networks (ICANN)*, Vienna, Austria, August 2001.

Postscript:
http://www.cs.ust.hk/~jamesk/papers/icann01.ps.gz

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