Tell me more ×
Cross Validated is a question and answer site for statisticians, data analysts, data miners and data visualization experts. It's 100% free, no registration required.
up vote 0 down vote favorite

SVM using RBF kernel is claimed to be similar (equivalent) to the K nearest neighbor classification method. I am not very clear about the analysis process of building this kind of relationship. Thanks for explanations.

share|improve this question
I am not aware of any claim that RBF SVM is similar to a K-NN classifier (it certainly isn't going to be equivalent). Can you give a reference to a place where such a claim has been made? – Dikran Marsupial Apr 20 '12 at 9:39
The similarity is that both classifiers calculate the distance between instances. In SVM these are the distances between the support vectors and new data and in kNN it is the distance between training all instances and new data. I think that's all. SVMs learn which support vectors they should choose and the number is usually much smaller than the size of the training set. kNN stores the whole training set. – alfa Apr 20 '12 at 17:08

Know someone who can answer? Share a link to this question via email, Google+, Twitter, or Facebook.

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Browse other questions tagged or ask your own question.