I'd like to know which exact version of svm is implemented in slearn. The references section on sklearn's svm page cites libsvm package and a paper from 1999 which is about comparing classification error rate and likelihood scores. The libsvm paper talks about the original svm paper by Vapnik. But I couldn't figure out which exact formulation of svm has been implemented.
The libsvm paper cites a paper titled Fast Training of Support Vector Machines Using Sequential Minimal Optimization that solves svm using a method called sequential minimal optimization. Is this the algorithm that sklearn actually calls under the hood or is it a different version of this method?