Hi Mathieu,
It looks interesting. Do you have in mind any specific real data we should
use to benchmark the methods?
Thanks,
Karol
2013/8/12 Mathieu Blondel <math...@mblondel.org>
> Hi Karol,
>
> Those would indeed be nice additions. However, we should do benchmarks on
> real data and focus on the most effective methods.
>
> I found this paper / software which could serve as a reference:
> http://jmlr.org/papers/v11/escalera10a.html
>
> Mathieu
>
> On Mon, Aug 12, 2013 at 1:27 PM, Karol Pysniak <kpysn...@gmail.com> wrote:
>
>> Hi All,
>>
>> Currently, scikit-learn uses randomly generated codebook for
>> error-correcting output-code (line 468 in sklearn/multiclass.py). However,
>> there are some interesting strategies we could use in sklearn. In
>> particular, I would like to start from trying:
>>
>> 1. BCH Codes as mentioned in section 2.3.4
>> http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume2/dietterich95a.pdf
>>
>> 2. Decriminant ECOC as presented in
>> O. Pujol, P. Radeva, , and J. Vitria`. Discriminant ECOC: A heuristic
>> method for application dependent design of error correcting output codes
>>
>> Also, for now we use only euclidean distance to find the nearest class as
>> represented in a codebook. We could add some new, for example, Humming
>> distance.
>>
>> What do you think about those new enhancements?
>>
>> Thanks,
>> Karol
>>
>>
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