Hi Steve,
Thank you very much for your reply. Could you please guide me to any helpful reference to learn about the other non-linear regression algorithms available in R language and about how I use any of them? Cheers,Amyate: Wed, 3 Feb 2010 10:59:27 -0500 > Subject: Re: [R] svm > From: mailinglist.honey...@gmail.com > To: amy_4_5...@hotmail.com > CC: r-help@r-project.org > > HI Amy, > > On Wed, Feb 3, 2010 at 1:56 AM, Amy Hessen <amy_4_5...@hotmail.com> wrote: > > > > Hi Steve, > > > > Could you please help me in this point?: > > > > I use SVM of R and Im trying some datasets from UCI but when I compare the > > results of my program( that does not do anything more than calling SVM) with > > the RMSE of SVM in any other paper, I found a big gap between them. > > > > For example, this is the rmse of svm of my program for the dataset bodyfat: > > 2.64561 > > > > And this is the RMSE of a paper 0.0204. > > > > Could you please tell me how I can reduce this gap in the performance of > > SVM? > > Sorry, it's hard to say w/o investing any real time to investigate > (and I unfortunately don't have the time to do so). > > There are different parameters you can play with in nu-regression vs. > eps-regression and different kernel functions that can be used that > might be a better fit for the type of data you are trying to learn > against. > > Before running the SVM (or any other "learning" alogorithm), there are > also ways to normalize your data, too .. > > Lots of things to look at ... > > -steve > > -- > Steve Lianoglou > Graduate Student: Computational Systems Biology > | Memorial Sloan-Kettering Cancer Center > | Weill Medical College of Cornell University > Contact Info: http://cbio.mskcc.org/~lianos/contact _________________________________________________________________ [[elided Hotmail spam]] [[alternative HTML version deleted]]
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