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From |
Nick Cox <[email protected]> |

To |
[email protected] |

Subject |
Re: st: Kernel methods / machine learning |

Date |
Fri, 8 Apr 2011 15:58:31 +0100 |

I don't think there's much that's visible. I'll guess wildly that there may be Stata stuff that is invisible to us, in the backrooms of some companies or agencies. But that's an known unknown to me at most. I think there are lots of people who would be interested in playing with this in an environment in which there was also serious data management, e.g. Stata. Most crime-solving TV stories would seem to depend on something similar! Nothing stops people writing code for this in Stata, except that to write a good set of supporting packages would seem to be a substantial undertaking. My main guess is that in practice almost everybody doing this -- and doing it visibly -- is probably doing it using some other language. I'd guess there is a whole heap more in MATLAB, for example. I think this stuff divides the world in various senses. One is on a small level. "ML" I take to mean "machine learning" in your posting but a lot of people on this list are likely to think "maximum likelihood". Another is on a larger level. Although lots of very smart people are involved, I get a sense of a search for a Holy Grail and a rather fickle field in which what is top and what is not varies very rapidly. I remember a time when people seemed to going round saying "neural nets" all the time, and so forth, but now we have different buzzwords. Reminds me of Bayesian stuff... Nick On Fri, Apr 8, 2011 at 3:37 PM, Diego Navarro <[email protected]> wrote: > 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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**References**:**st: Kernel methods / machine learning***From:*Diego Navarro <[email protected]>

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