[R] svm

[Achim Zeileis]

On Thu, 4 Feb 2010, Amy Hessen wrote:

>
>
> 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?

There are a few papers in the Journal of Statistical Software that might 
be interesting for you. The paper about the "caret" package gives a good 
overview, many further pointers, and an easy-to-use interface (see
http://www.jstatsoft.org/v28/i05/). There is also a comparison of Support 
Vector Machines in R (in http://www.jstatsoft.org/v15/i09/). Further 
interesting issues might be kernlab (http://www.jstatsoft.org/v11/i09/) or 
glmnet (http://www.jstatsoft.org/v33/i01/) among others.

See also the Machine Learning task view

   http://CRAN.R-project.org/view=MachineLearning

for other approaches and their implementations.

hth,
Z

> Cheers,Amyate: Wed, 3 Feb 2010 10:59:27 -0500
>> Subject: Re: [R] svm
>> From: mailinglist.honeypot at gmail.com
>> To: amy_4_5_84 at hotmail.com
>> CC: r-help at r-project.org
>>
>> HI Amy,
>>
>> On Wed, Feb 3, 2010 at 1:56 AM, Amy Hessen <amy_4_5_84 at hotmail.com> wrote:
>>>
>>> Hi Steve,
>>>
>>> Could you please help me in this point?:
>>>
>>> I use SVM of R and I?m 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
>
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