On Wed, Aug 8, 2012 at 8:50 PM, Andreas Müller wrote:
>
> 2) There are at the moment no plans to add structured SVMs to the library.
> The reason is that structured
> models usually are very problem specific. It is possible to build generic
> frameworks like Joachsim SVMstruct,
> which works by th
seems hard to integrate nicely.
>>
>> Btw, I have some structured SVM code to play around in Python, if you
want:
>>
http://peekaboo-vision.blogspot.co.uk/2012/06/structured-svm-and-structured.html
>>
>> Cheers,
>> Andy
>>
>>
>> - Ursprün
In fact I wanted to estimate plane parameters for small patches using
structured output prediction. But, my dataset is very noisy and I had not
enough time to do that ( choosing kernels, parameters, cross validation and
etc).
I decided to estimate the depth at each point and smooth it by a CRF.
As
>
> Thanks for the fast response.
>
>
> to JP: It works for me using gcc and g++ on 32-bit Mac and Linux! :)
>
>
> J. Friedman in the paper "Greedy Function Approximation: A Gradient
> Boosting Machine" has mentioned the M-regression algorithm which is
> a gradient boosting regression method
-
> Von: "amir rahimi"
> An: scikit-learn-general@lists.sourceforge.net
> Gesendet: Mittwoch, 8. August 2012 12:40:52
> Betreff: [Scikit-learn-general] GradientBoostingRegression loss function
> andStructured svm
>
>
>
> Hi all,
> I have two questi
ant:
http://peekaboo-vision.blogspot.co.uk/2012/06/structured-svm-and-structured.html
Cheers,
Andy
- Ursprüngliche Mail -
Von: "amir rahimi"
An: scikit-learn-general@lists.sourceforge.net
Gesendet: Mittwoch, 8. August 2012 12:40:52
Betreff: [Scikit-learn-general] GradientBoostingRegression lo
Hi all,
I have two questions/requests
Is there any way to define arbitrary loss function for gradient boosting
regression? e.g. using huber penalty
My request is about adding structured output prediction for SVM in the
library. Is there any plan for adding that?
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