Hi, this is a question about the R package kernlab.
I use kernlab as a library in a C++ program. The host application
defines a graph kernel (defined by me), generates a gram matrix and
trains kernlab directly on this gram matrix, like this:
regm<-ksvm(K,y,kernel="matrix"),
where K is the n x n gram kernelMatrix of my kernel, and y is the
R-vector of quantitative target values.
So, to make sure you got it: I don't want kernlab to compute the kernel
values by itself. Rather, this is a task for the host application.
Learning (see above) works well, but how do I predict a new instance? I
couldn't find any information in this respect in the manual. The only
examples for prediction were concerned with data from the input space,
which i don't have, since my input space consists of graphs. I tried the
following:
predict(regm,x,type="response")
where x is the 1xn R-matrix containing kernel values between the
instance to be predicted and my training points. This won't work:
Error in as.matrix(Z) : object "Z" not found.
I'm using the current CRAN version of kernlab. Any help by kernlab users
who had a similar task to do would be appreciated.
Best regards,
Andreas Maunz
--
http://www.maunz.de
Yoda of Borg are we: Futile is resistance. Assimilate you, we will.
