**Hey, Here is my problem,**

Given a set of documents I need to assign each document to a predefined category.

I was going to use the n-gram approach to represent the text-content of each document and then train an SVM classifier on the training data that I have.  

Correct me if I miss understood something please.

The problem now is that the categories should be dynamic. Meaning, my classifier should handle new training data with new category.

**So for example,** if I trained a classifier to classify a given document as category A, category B or category C, and then I was given new training data with category D. I should be able to incrementally train my classifier by providing it with the new training data for "category D".

**To summarize, I do NOT want to combine the old training data (with 3 categories) and the new training data (with the new/unseen category) and train my classifier again. I want to train my classifier on the fly**

Is this possible to implement with SVM ? if not, could u recommend me several classification algorithms ? or any book/paper that can help me.

Thanks in Advance.