Multiple-feature combination for support vector machines

Question

I have two types of feature vectors for a dataset. Both types of the feature vectors could give an predicting accuracy about 90% by training a SVM.

To achieve higher accuracy, I plan to combine the two types of feature vectors.

My question is which of the two following strategies I should take:

1. Train one SVM for each type of feature vectors, and then combine the prediction results linearly.
2. Merge the two types of feature vectors into a longer one, and then train a SVM.

Answer 1

There's no way of telling which one will get you better accuracy. Simply try and see :)