Hi,
More the features more sparse the data becomes and makes easier for
classification. SVM works in higher dimensions and should, therefore, be
not a problem.
The only problem I percieve is the time to analyze.
As regrards the discussion on kernel type,I found that RBF takes very less
time as compared to polynomial.
Best wishes
Ajay Mathur
University of Southampton
U.K
Quoting Monika Ray <[log in to unmask]>:
> Hello,
>
> SVM is known to defeat the curse of dimensionality...then why is it so
> that having a large number of attributes/features in the data is not
> desirable when using SVM?
>
> I thought it should not matter....where am I getting confused?
>
> Thank You.
>
> Sincerely,
> Monika Ray
>
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