how to explain the behaviour: linear svm does better than non-linear RBF I am working on a binary class classification problem. 
Each sample is a vector 1x101, I have a lot of data samples more than 150k 
I tried training a linear svm and a non-linear svm (RBF) "zscore normalization is used in both cases". surprisingly, the linear does better than the svm (RBF).
I am trying to explain this by considering the following points:


I beleive that the quality of my feature is not very good. 
I think the nonlinear case experinces a kind of overfitting.


my question is how to explain this behaviour?!! does what I am thinking in make sense?!! I am thinking in using Adaboost to perform  the training, is it a good idea or not?
The quality of your features might actually be better than you think. If they provide linear separability, nonlinear kernels will overfit more readily than a linear kernel, leading to your result.