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I'm using a SVM classifier with a weighted RBF kernel. My dataset has 17 features. In the RBF kernel I will use a weight for each feature. Of course the weights must sum to one. For choosing the best weights, I'm using random search.

Should I you use in this case the stick-breaking process or the Dirichlet pseudo random number generator?

Second, should I vary the weights only around 1/17 (e.g. with a normal distribution) or should I allow some few weights to become very large and the others very small.

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