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st: Kernel methods / machine learning


From   Diego Navarro <the.electric.me@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: Kernel methods / machine learning
Date   Fri, 8 Apr 2011 11:37:16 -0300

Are there any Stata packages in the works for kernel trick methods
such as kernel PCA/kernel SVM?

I know SVM isn't Stata's cup of tea, but I really need kernel PCA
these days, and the jerry-rigged code I hacked together for one
project is pretty brittle, doesn't interact well with other Stata
commands, will break at missing data and ill-behaved formulas, etc.
etc.  (Besides, it's so messy that I don't know for a fact that it
conforms to the idea of kernel PCA everyone else is expecting).

I did try "search kernel, net" and such, but there are just too many
results. To ask too wide a question, do you think there's a future in
Stata for machine learning methods? I came to Stata from econometrics,
but my work is increasingly "ML-enhanced econometrics", and while I
learned R (which has the basic kernel trick packages, for example), I
don't care much for its ad-hockish palette of CLOS-like object
structures.
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