Can an SVM learn incrementally?

Question

I am using a multi-dimensional SVM classifier (SVM.NET, a wrapper for libSVM) to classify a set of features.

Given an SVM model, is it possible to incorporate new training data without having to recalculate on all previous data? I guess another way of putting it would be: is an SVM mutable?

Answer 1

Actually, it's usually called incremental learning. The question has come up before and is pretty well answered here : A few implementation details for a Support-Vector Machine (SVM).

In brief, it's possible but not easy, you would have to change the library you are using or implement the training algorithm yourself.

I found two possible solutions, SVMHeavy and LaSVM, that supports incremental training. But I haven't used either and don't know anything about them.