AFAIK, SVM can't deal with nested features and/or missing features. You
probably need to manually encode your features to fixed-length array
according to your particular setting.
On Tue, Mar 27, 2012 at 9:40 AM, Dushyant Arora
<[email protected]>wrote:
> Here is what my dataset looks like:
>
> I do periodic polling/scan of a particular data and it returns back some
> features like
> [1, 2, 3]
> [4, 5, 6]
> etc
> The number of features are always 3 but the number of lists returned may
> be different. Say in first scan I get back 10 lists, in second I get 4
> lists etc.
> Now I want to use SVM to distinguish between two such scans/polls. So, I
> want to give the following training data:
>
> X = [[[1,2,3], [4,5,6]], [[7,8,9]], [[3,7,1],[7,8,3]]]>>> Y = [1, 1, 2]>>>
> clf = svm.SVC()>>> clf.fit(X, Y)SVC(C=1.0, cache_size=200, coef0=0.0,
> degree=3, gamma=1.0, kernel='rbf', probability=False, scale_C=False,
> shrinking=True, tol=0.001)*
> *
>
> *But I get an error when I run this. How can I use my data with multi-class
> SVMs*
>
> *
> *
>
> *Thanks,*
>
> *Dushyant*
>
>
>
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--
Best Wishes
--------------------------------------------
Meng Xinfan（蒙新泛）
Institute of Computational Linguistics
Department of Computer Science & Technology
School of Electronic Engineering & Computer Science
Peking University
Beijing, 100871
China
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