I have two data set with different size.

1) Data set 1 is with high dimensions 4500 samples (sketches).

2) Data set 2 is with low dimension 1000 samples (real data). I suppose that "both data set have the same distribution"

I want to train an non linear SVM model using sklearn on the first data set (as a pre-training ), and after that I want to update the model on a part of the second data set (to fit the model). How can I develop a kind of update on sklearn. How can I update a SVM model?

In sklearn you can do this only for linear kernel and using SGDClassifier (with appropiate selection of loss/penalty terms, loss should be hinge, and penalty L2). Incremental learning is supported through partial_fit methods, and this is not implemented for neither SVC nor LinearSVC.

Unfortunately, in practise fitting SVM in incremental fashion for such small datasets is rather useless. SVM has easy obtainable global solution, thus you do not need pretraining of any form, in fact it should not matter at all, if you are thinking about pretraining in the neural network sense. If correctly implemented, SVM should completely forget previous dataset. Why not learn on the whole data in one pass? This is what SVM is supposed to do. Unless you are working with some non-convex modification of SVM (then pretraining makes sense).

To sum up:

• From theoretical and practical point of view there is no point in pretraining SVM. You can either learn only on the second dataset, or on both in the same time. Pretraining is only reasonable for methods which suffer from local minima (or hard convergence of any kind) thus need to start near actual solution to be able to find reasonable model (like neural networks). SVM is not one of them.
• You can use incremental fitting (although in sklearn it is very limited) for efficiency reasons, but for such small dataset you will be just fine fitting whole dataset at once.