| Abstract |
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In Support Vector (SV) regression, a parameter \math controls the number of Support Vectors and the number of points that come to lie outside of the so-called \math-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of \math that lead to the lowest generalization error. We find good agreement with the values that had previously been predicted by a theoretical argument based on the asymptotic efficiency of a simplified model of SV regression.
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 Additional Information
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Citation:
Athanassia Chalimourda, Bernhard Scholkopf, Alex J. Smola,
"Choosing in Support Vector Regression with Different Noise Models: Theory and Experiments,"
ijcnn,
p. 5199,
IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 5,
2000
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