Hi!
I have a questionaccording to classifing new examples from an already trained svm. I
have tarined a svm (e1071 package) with a training set of 1526 examples.
Now I have another data set with 2163 examples and I want to use the already trained
svm for prediction:
pred<-predict(a.svm,newdata);
Afterwards for further processessing I add further information to the results of teh
classification from the original input (also 2163 x N matrix)
result<-as.data.frame(cbind(newdata[81:84],pred))
But I receive an error:
Error in data.frame(..., check.names = FALSE) :
arguments imply differing number of rows: 2163, 1526
So I wonder why the svm is stick to a resultset of 1526 although I put in 2613
examples? To my mind I thought that the classification/predictin depends on the length
of the feature vector not on the number of examples? Or am I doing something wrong?
Thanks
--
Frank G. Zoellner
AG Angewandte Informatik
Technische Fakult"at
Universit"at Bielefeld
phone: +49(0)521-106-2951
fax: +49(0)521-106-2992
email: [EMAIL PROTECTED]
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