[Corpora-List] Coefficients in SVM models

[Maximilian Haeussler]

Hi Ken,
random thought: in an environment like Weka, R or sklearn, you can change
your classifier to a regression or decision tree based classifier by
changing just a single line in your code. The weights of the regression and
the decision tree are easy to interpret.
You could use the regression for the analysis of the feature influence,
while still doing the final classification with the SVM, (in case that the
SVM is really far superior to the regression).
You could use a lasso or elasticnet regressor and increase alpha to remove
features, just like the L1 parameter for SMV's suggested by Hady.

cheers
Max

On Thu, Aug 20, 2015 at 8:36 PM, Ken Litkowski <ken at clres.com> wrote:


> Are there any methods in computational linguistics for interpreting the
> coefficients in SVM models? I have 120 nice preposition disambiguation
> models developed using the Tratz-Hovy parser, with an average of about
> 15,000 features for each preposition. I'd like to identify the significant
> features (hopefully lexicographically salient). One such method
> (implemented in Weka) is to square the coefficients and to use this as the
> basis for ranking the features (the source of this method being a classic
> study by Guyon et al., 2002, in gene selection for cancer classification
> using support vector machines
> <http://link.springer.com/article/10.1023/A:1012487302797>). I'm
> extending these models (which make heavy use of WN) with other lexical
> resources, including FN, VN, and CPA. This will make the feature space even
> more hyperdimensional, so I'd like to pare them back in a principled way so
> I can see the potential contribution of these other resources.
>
> Thanks,
>     Ken
>
> --
> Ken Litkowski                     TEL.: 301-482-0237
> CL Research                       EMAIL: ken at clres.com
> 9208 Gue Road                     Home Page: http://www.clres.com
> Damascus, MD 20872-1025 USA       Blog: http://www.clres.com/blog
>
>
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