SVM is reasonable. SGD with hand-tuning of the learning parameters may work.
With so little training data, you will have a difficult assessing whether your system is working. Sometimes, you can rephrase your problem so that all of your training data across many situations can be pooled together. There is a nice paper on google priority mail about just such an example where Google used meta-features so that they could train a few models across all users On Mon, Sep 12, 2011 at 6:52 AM, Loic Descotte <[email protected]>wrote: > > My classification problem is very similar to the "20 newsgroups" example. > But I don't have the possibility to use a large quantity of data for > training. > ... > I'd like to try with 10 examples by category (with 2 or 3 category), > choosing good examples with the more frequent keywords to be sure that the > learning phase will be efficient. > > Can it be relevant with so little data ? > >
