Does SVM perform poorly when fat-tailed data with outliers is used? What are some things that could be done to improve learning with such data? Does the choice of kernel and/or kernel parameter change?

For dealing with outliers, I was thinking of transforming the data such that values +/- 2 standard deviations are assumed to be outliers and would be replaced with the closest marginal values. (then transform the data into the range [-0.9, 0.9])

Assuming fat-tails are undesireable with SVM, what are some transformations that could be considered to help alleviate the fat-tails?

(I'm using LIBSVM with the Matlab interface)


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.