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?
Actually, it's usually called incremental learning. The question has come up before and is pretty well answered here : http://stackoverflow.com/questions/3446622/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.