Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

June 2007, Vol. 19, No. 6, Pages 1633-1655
(doi: 10.1162/neco.2007.19.6.1633)
© 2007 Massachusetts Institute of Technology
Efficient Computation and Model Selection for the Support Vector Regression
Article PDF (154.56 KB)
Abstract

In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR). We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.