I developed the code for these questions in R. For the SVM with RBF I call svmlib through an interface with R (in concrete, I use the library e1071, strange name...). This lib has options for working with the RBF kernel.

For the "regular" RBF, I implement step-by-step Lloyd´s algorithm, assemble the matrix

(RBF lecture 16, slide 14) and calculate the trained

**w**.

I use the same N=100 points with both algorithms, obviously. And classify with both methods (SVM and "regular").

Regarding time, R it's not too bad. Running a batch of 1000 sets of 100 points takes me perhaps 2 minutes or less (my home desktop has a low end AMD A8 3850 processor, with 8 GB RAM, nothing fancy; it's quad-core but only one core is used by R). I use 200 random points for testing (the same for all the 1000 batches with 100 points... random is random...).

Regarding correctness, I verified the code with some care, (about 100 lines in R, with some amount of vectorization, for the implementation of both SVM and regular RBF) and the results obtained for Ein, Eout, etc... seem reasonable. So, I cross my fingers...

I didn't look for a library implementing "regular" RBF. Perhaps there are some out there... but it seems to me that it is not a very popular approach to classification

...