Sharing of a trained SVM model I have been asked by a journal reviewer to provide the my final trained SVM models so that others can try to replicate or make use of the models.
Is there an accepted way to do this? 
I was thinking of providing a spreadsheet detailing the selected features, the support vectors, the hyperplane bias and he support vector weights for each model. I am using Matlab's *SVM_train* function for this purpose.
If have done this in the past for regression and regularized dircriminant models however this seems rather unwieldy for SVMs.
 A: I can't see why you can't just save the model in a .mat file and make that available.  It would be far more sensible though for the reviewer to request the code that you used to make the model, that would be much more useful from a reproducibility perspective.  A MATLAB function that takes the data as input and deterministically generates the model that you used would be an excellent way of "providing the model" as anyone who is interested can easily try it out with their version of the data.
For my papers I try to integrate the code that generates the results with the LaTeX source of the paper, so that in principle, anyone can reproduce the work by typing "make" and it will re-run the experiment, patch the results into the LaTeX source and recompile the document.  Rather a lot of work is involved in this, but I think it leads to better experimental work in the long run.
A: That is kind of an odd request, but I did something similar when participating with a BioCreative classification task. I basically requested a port facing outside of the University firewall and pointed an XMLRPC server at that port. My classifier lived on the University server, but users were still able to send classification requests to it, once they knew the address. This is actually quite easy to do with Python (SimpleXMLRPCServer), and then you can just publish some code snippets for users to use to connect with your classifier onto GitHub!
