The dataSet contains 213 examples of 7 classes . Each example are 25000 features. I want to learn model with SVM (test scenario used are 10-fold cross validation). I am a beginner in machine learning, i want to know what type of kernel i should use in the case of my data: linear or no-linear (as RBF kernel). Also, I want some tips to choose the value of parameter C. Thanks!
By elements you mean samples/examples right?
If your dataset consists of 213 samples of 25000 features each you have a much more serious problem to solve than the selection of your SVM hyper-parameters.
In short 213 samples provide are very few for the dimensionality of your problem. In high dimensional spaces you typically need lots of data have a sufficient representation of the space (and this is just one of the problems associated with high-dimensional data). The main issue you have to deal with is feature selection, in order to reduce the number of features.
Now to answer your question:
If I were to choose, I'd take a linear kernel in such a problem. The last thing you need is to project the data to an even higher dimensional space.