Hello,
Indeed, SVM methods only work with positive definite kernels (as all kernel
methods in Machine Learning). This is because you can view pd kernel as
inner product, which you can't if the kernel isn't pd. Kernel methods
(including SVM) "only" replace inner products with kernels, thus mapping
the space of the features to a usually larger, more complex feature space
as defined by the kernel function.
Hope that clarifies,
N
On 29 May 2014 07:56, Benjamin Li <[email protected]> wrote:
> Dear all,
>
> I am new to scikit learn and I want to ask a question about the SVM module.
> Does the SVM module support non-psd kernel matrix?
>
> Ive found that in the web page ("
> http://scikit-learn.org/stable/modules/svm.html";) there are lines about
> the kernel matrix:
>
> >1.2.7.1. SVC
> >....
> > Q is an n by n positive semidefinite matrix, Q_{ij} \equiv K(x_i, x_j)
> and \phi (x_i)^T \phi (x) is the kernel.
>
> Does this imply that the SVC module only support psd kernel matrix?
>
> Thank you.
>
> Best Regards,
> Benjamin Li
>
>
> ------------------------------------------------------------------------------
> Time is money. Stop wasting it! Get your web API in 5 minutes.
> www.restlet.com/download
> http://p.sf.net/sfu/restlet
> _______________________________________________
> Scikit-learn-general mailing list
> [email protected]
> https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
>
>
------------------------------------------------------------------------------
Time is money. Stop wasting it! Get your web API in 5 minutes.
www.restlet.com/download
http://p.sf.net/sfu/restlet
_______________________________________________
Scikit-learn-general mailing list
[email protected]
https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
