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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 all previous data? I guess another way of putting it would be: is an SVM mutable?

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It is called incremental learning. 

A few implementation details for a Support-Vector Machine (SVM).

Generally 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. More details on this will be given by studying the SVM Algorithms.

Hope this answer helps you!

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