the error occurred while selecting feature using recursive feature elimination in sklearn
Recursive feature elimination works by considering co-efficient value of feature column (in case of linear models) or variable importance exposed, by say, random forest classifier.

For example, in case of a linear model, it will eliminate feature with smallest (absolute) value of co-efficient (i.e. less impact on outcome).

In case of SVMs with non-linear kernels, the classification is happening in a transformed space of features and not original features. So it is not possible to assign importance measure to original features. So RFE doesn't work with RBF kernel here.

Related question on variable weight for non-linear kernel