Re: MLlib - Possible to use SVM with Radial Basis Function kernel rather than Linear Kernel?
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
Re: MLlib - Possible to use SVM with Radial Basis Function kernel rather than Linear Kernel?
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
Re: MLlib - Possible to use SVM with Radial Basis Function kernel rather than Linear Kernel?
We don't support kernels because it doesn't scale well. Please check When to use LIBLINEAR but not LIBSVM on http://www.csie.ntu.edu.tw/~cjlin/liblinear/index.html . I like Jey's suggestion on expanding features. -Xiangrui On Thu, Sep 18, 2014 at 12:29 PM, Jey Kottalam j...@cs.berkeley.edu wrote: 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 - To unsubscribe, e-mail: user-unsubscr...@spark.apache.org For additional commands, e-mail: user-h...@spark.apache.org
MLlib - Possible to use SVM with Radial Basis Function kernel rather than Linear Kernel?
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
