"If we compare to non-kernel polynomial regression it is O(Tnp) where is p is dimension of polynomial while kernel polynomial is O(n^2d) + O(T*n^2) where d is original number of attributes, where T is number of epochs"
Does the above observation makes sense, it looks very counter-academic given so much praises for SVM and Kernel tricks.
