Hi Aris, A simple approach to gaining some of the benefits of an RBF kernel is to add synthetic features to your training set. For example, if your original data consists of 3-dimensional vectors [x, y, z], you could compute a new 9-dimensional feature vector containing [x, y, z, x^2, y^2, z^2, xy, xz, y*z].
This basic idea can be taken much further: 1. http://www.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf 2. http://arxiv.org/pdf/1109.4603.pdf Hope that helps, -Jey On Thu, Sep 18, 2014 at 11:10 AM, Aris <arisofala...@gmail.com> wrote: > Sorry to bother you guys, but does anybody have any ideas about the status > of MLlib with a Radial Basis Function kernel for SVM? > > Thank you! > > On Tue, Sep 16, 2014 at 3:27 PM, Aris < wrote: > >> Hello Spark Community - >> >> I am using the support vector machine / SVM implementation in MLlib with >> the standard linear kernel; however, I noticed in the Spark documentation >> for StandardScaler is *specifically* mentions that SVMs which use the RBF >> kernel work really well when you have standardized data... >> >> which begs the question, is there some kind of support for RBF kernels >> rather than linear kernels? In small data tests using R the RBF kernel >> worked really well, and linear kernel never converged...so I would really >> like to use RBF. >> >> Thank you folks for any help! >> >> Aris > > --------------------------------------------------------------------- To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
