> On Dec 2, 2014, at 6:34 AM, Andy <t3k...@gmail.com> wrote:
>
> Hi Ilya.
>
> Thanks for your interest in contributing.
> I am not expert in affinity propagation, so it would be great if you could
> give some details of what the advantage of the method is.
> The reference paper seems to be an arxiv preprint with 88 citations, which
> would probably not qualify for inclusion in scikit-learn,
> see the FAQ
> http://scikit-learn.org/dev/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published
>
> <http://scikit-learn.org/dev/faq.html#can-i-add-this-new-algorithm-that-i-or-someone-else-just-published>
Wow, I had not seen this FAQ. "As a rule we only add well-established
algorithms. A rule of thumb is at least 3 years since publications, 1000+ cites
and wide use and usefullness.” I was intending to contribute a rule learning
system to scikit-learn, and/or descriptive learning methods. I guess those
are both right out. I thought scikit-learn would welcome some variety but
1000+ cites (sic) and wide use pretty much rules out anything but statistical
learning. Among symbolic methods there is only one rather mediocre decision
tree induction method.
Anyone know of another python framework that’s a little more welcoming?
-Tom
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