I have trained & validated a SVM using the scikit-learn SVC (RBF) class without
problems, but my final deployment of the classifier is to be in a body of C++
code using libsvm only, no Python, and this is an existing system reading the
libsvm .model sparse format text file. To save retraining using the libsvm
command line tools, I'm considering implementing a SVC method to write the
equivalent libsvm .model file from the SVC attributes such as support_vectors_
etc. I wanted to ask the experts about the feasibility, particularly if there
are pitfalls I'm not aware of? That does assume that the support vectors would
themselves be compatible, given libsvm as the common code base?
Leigh
--
Leigh M. Smith
mailto:le...@leighsmith.com
http://www.leighsmith.com
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