string kernels for GP/SVM regression

Question

I want to solve a small regression problem where the inputs are variable-length strings from a small vocabulary. I'd like to use Gaussian Process regression with some kind of string kernel. (SVM regression also ok.)

I see from this page that shogun supports many kinds of string kernels - can someone please provide a high level summary (with references to papers) of how they work?

I'd also like to see a worked example (in python), since I've never used shogun before. I found this post on stackoverflow, but it's dated from 2014, and it's not clear if the interface is up to date.

Thanks Kevin

Answer 1

The documentation pages of string kernel classes contain the information you are looking for. For example:

• http://www.shogun-toolbox.org/api/latest/classshogun_1_1CPolyMatchStringKernel.html contains a high-level summary.
• http://www.shogun-toolbox.org/api/latest/classshogun_1_1CSalzbergWordStringKernel.html refers to the paper.

Quite likely not all classes will contain either one piece of information or the other.