# RBF kernel not working for SVM

I was trying to use RBF kernel for a 3 way classification of data. However, the problem is that when I use RBF kernel all the examples are classified as one class. Here is the confusion matrix for RBF kernel

[[ 0  0 19]
[ 0  0 12]
[ 0  0 19]]


But when I use linear kernel i get a decent accuracy of 80%. I tried the suggestions mentioned in this answer but none of them helped. The dataset contains 500 samples and I have used 3 features. T always thought that when the features are lesser than number of samples rbf will give the best performance but this doesn't seem the case here.

• You're not really giving enough information here - how are you doing the training? Perhaps you're not including sufficiently broad values for the kernel width? – MotiN Jul 22 '18 at 8:41
• Could you kindly elaborate the meaning of the statement "how are you doing the training"? – Rohan Akut Jul 24 '18 at 21:35