Hi All,

Sorry if this is a repost (a quick browse didn't give me the answer).

I wonder if there are packages that can do the feature selection and
classification at the same time. For instance, I am using SVM to classify my
samples, but it's easy to get overfitted if using all of the features. Thus,
it is necessary to select "good" features to build an optimum hyperplane
(?). Here is a simple example: Suppose I have 100 "useful" features and 100
"useless" features (or noise features), I want the SVM to give me the
same results when 1) using only 100 useful features or 2) using all 200
features.

Any suggestions or point me to a reference?

Thanks in advance!

Frank

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