Great work indeed! Thanks to everybody involved!

BTW: I find Gael's suggestion to include comparisons of different
methods and giving rule of thumbs on how to
choose between them a great idea. Maybe we could adopt that throughout
the scikit... (something like Table 10.1 in ESLII, Section
'Off-the-shelve procedures for Data Mining' comes into my mind)

2011/9/16 Matthieu Brucher <[email protected]>:
> Excellent, indeed!
>
> Perhaps a small enhancement: Isomap is not an extension of MDS, it is "just"
> another MDS. The classical MDS is PCA, and Isomap is a metric MDS (it's the
> official term found in the litterature).
>
> Cheers et keep up the good work!
>
> Matthieu
>
> 2011/9/16 xinfan meng <[email protected]>
>>
>> Fantastic indeed!
>>
>> BTW, the illustration picture in
>> http://scikit-learn.sourceforge.net/dev/auto_examples/manifold/plot_compare_methods.html
>> seems to be positioned incorrectly.
>>
>> On Thu, Sep 15, 2011 at 10:45 PM, Gael Varoquaux
>> <[email protected]> wrote:
>>>
>>> I am just writting an email advertising the manifold learning part of the
>>> scikit, and I linked to the docs:
>>> http://scikit-learn.sourceforge.net/dev/modules/manifold.html
>>>
>>> All I must say it: wow fantastic docs. I remembered that it was good, but
>>> coming back to it makes me think that it is absolutely an ideal example
>>> of good docs:
>>>
>>>  - A leading self-explaining summary figure (with the code generating it,
>>>   of course)
>>>  - An outline that match quite clearly the different main topics of the
>>>   example
>>>  - Each part giving intuitions and an example, before discussing the
>>>   computational complexity
>>>  - Practical tips (very important)
>>>
>>> The only thing that I could see that could be added would be a table
>>> comparing the different methods and giving rule of thumbs on how to
>>> choose between them.
>>>
>>> Great work, Jake and the others! You rock absolutely. Thank you so much,
>>> in the name of the users who will read these docs, and never actually
>>> come on the mailing list to thank you.
>>>
>>> Gael
>>>
>>>
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>>
>>
>>
>> --
>> Best Wishes
>> --------------------------------------------
>> Meng Xinfan(蒙新泛)
>> Institute of Computational Linguistics
>> Department of Computer Science & Technology
>> School of Electronic Engineering & Computer Science
>> Peking University
>> Beijing, 100871
>> China
>>
>>
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>
>
>
> --
> Information System Engineer, Ph.D.
> Blog: http://matt.eifelle.com
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>
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-- 
Peter Prettenhofer

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