Hi Mathieu,
Thanks for the suggestions, I'll test the methods and get back with the results.
Thanks,
Karol
On Aug 12, 2013, at 7:55 PM, Mathieu Blondel <math...@mblondel.org> wrote:
> Hi Karol,
>
> I would do the benchmark on commonly-used datasets such as MNIST, USPS,
> News20, Covertype, Sector, etc.
>
> http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/
>
> Note that ECOC can potentially improve accuracy on binary classification too,
> so I would do benchmarks on binary classification datasets as well.
>
> Thanks for your interest in improving our ECOC classifier!
>
> Mathieu
>
> On Tue, Aug 13, 2013 at 1:25 AM, Karol Pysniak <kpysn...@gmail.com> wrote:
> 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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