Choosing nu in support vector regression with different noise models: theory 
and experiments

Chalimourda, A; Schölkopf, B; Smola, AJ

doi:10.1109/IJCNN.2000.861457

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Choosing nu in support vector regression with different noise models: theory and experiments

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引用

Chalimourda, A., Schölkopf, B., & Smola, A. (2000). Choosing nu in support vector regression with different noise models: theory and experiments. In IEEE-INNS-ENNS International Joint Conference on Neural Networks: IJCNN 2000: Neural Computing: New Challenges and Perspectives for the New Millennium (pp. 199-204). Piscataway, NJ, USA: IEEE.

引用: https://hdl.handle.net/11858/00-001M-0000-0013-E5C3-3

要旨

In support vector (SV) regression, a parameter /spl nu/ controls the number of support vectors and the number of points that come to lie outside of the so-called /spl epsi/-insensitive tube. For various noise models and SV parameter settings, we experimentally determine the values of /spl nu/ 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.