Christoph Helma wrote:
>
> Hallo,
>
> I have a question concerning SVM regression in R. I intend to use SVMs for feature
>selection (and knowledge discovery). For this purpose I will need to extract the
>weights that are associated with my features. I understand from a previous thread on
>SVM classification, that predictive models can be derived from SVs, coefficiants and
>rhos, but it is unclear for me how to transfer this information to the regression
>problem. Can anyone help in this respect (I am *not* an SVM expert)?
That's pretty simple.
The ``decision'' (predictor) function for regression is as follows:
f(x) = \sum_{i=1}^{l} alpha_i * K(x_i, x) - rho
where `alpha_i' are the coefficients of the SVs, `x_i' are the SVs
themselves, and `l' the number of SVs.
Note that `rho' must be *substracted* because libsvm returns -b for some
reasion.
Best,
David.
>
> Thanks,
> Christoph
> --
> :: christoph helma
> :: computational toxicologist
> :: university freiburg
> :: georges koehler allee 079, d-79110 freiburg/br
> :: phone ++49-761-203-8013, fax -8007
> :: [EMAIL PROTECTED]
> :: http://www.informatik.uni-freiburg.de/~helma/
>
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