SVM and concatenation of features

For example I train my SVM on 3 features set of different size:

Training data size [rows cols]:

1. features1 [nsamples 100]
2. features2 [nsamples 50]
3. features3 [nsamples 128]

And for each feature set I get accuracy >0.5. I assume that combining features by concatenating them [nsamples 278] will give me better results then any of feature set separately.

Is that assumption always true?