Relational Fisher Kernel Implementation

Question

I was reading the paper on Relational Fisher Kernel which involves Bayesian Logic Programs to calculate the Fisher score and then uses SVM to obtain the class labels for each data item.

I don't have strong background from Machine learning. Can someone please let me know about how to go about implementing an end-to-end Relational Fisher Kernel and what sort of input would it expect? I could not find any easy step-by-step flow showing this implementation. I am ok with using libraries for SVM etc. (e.g. libsvm), but I would like to know the end-to-end flow (in as easy language as possible). Any help will be highly appreciated.

Answer 1

libsvm does not implement the Relation Fisher Kernel, however, you can calculate the Fisher information matrix as described in the paper, and the use it as the precomputed kernel input to libsvm. See: using precomputed kernels with libsvm