I am classifying webpages based on several features of HTML-structure. I get the best cross-validation results with the RBF-kernel.
With a linear kernel, if I understand correctly, it is possible to attribute importance to certain features in the feature vector (How does one interpret SVM feature weights?). This would help in correcting the model for certain specific misclassifications (finding the features that 'are to blame' for the mistake). Am I correct when I say that this is not possible for RBF kernels?
If so, is there a good way of correcting the model for specific documents that get wrong classifications?