Dear R-community,
I was using SVM regression (svm {e1071}) for predictions of single
soil properties of a huge data set (3000 samples). There are for the
eps-regression using the radial basis kernel three optimization
parameters needed.
To make things easier (using only two optimization parameters and not
loosing performance) I wanted to use LS SVM regression
(lssvm{kernlab}). But it looks to me that it is not yet implemented.
At least I got error messages, which I could not find a solution for
(Error in if (n !_dim(y)[1] stop ("Labels y and data x dont match").
Otherwise I could not find another LSSVM regression implementation in
R, or is there?
Regards,
Thomas
Dr. Thomas Terhoeven-Urselmans
Post-Doc Fellow
Soil infrared spectroscopy
World Agroforestry Center (ICRAF)
United Nations Avenue, Gigiri
PO Box 30677-00100 Nairobi, Kenya
Ph: 254 20 722 4113 or via USA 1 650 833 6654 ext. 4113
Fax 254 20 722 4001 or via USA 1 650 833 6646
Email: t.urselmans at cgiar.org
Internet: http://worldagroforestrycentre.org
Least-square support vector machines regression!
4 messages · Max Kuhn, Thomas Terhoeven-Urselmans
To make things easier (using only two optimization parameters and not
loosing performance) I wanted to use LS SVM regression (lssvm{kernlab}). But
it looks to me that it is not yet implemented. At least I got error
messages, which I could not find a solution for (Error in if (n !_dim(y)[1]
stop ("Labels y and data x dont match").
I've used the lssvm function in kernlab without issue. You should follow the posting guide and provide a reproducible example so that there is a possibility of answering your question. Plus, what versions etc. Max
5 days later
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