Hi,
Have you tried to ask the authors for their source code?
If your implementation is based on scikit-learn, it could be interesting to
share it as a gist.
I had a quick look at the paper and it seems to me that you don't even need
to change liblinear. You just need to prepare a sparse matrix X of shape
[n_samples * n_clusters, n_features * (n_clusters + 1)] and a y vector of
shape [n_samples * n_clusters]. Then you can fit a LinearSVC with X and y.
Of course, you do need to re-implement the predict method but no direct
changes to liblinear are needed.
M.
On Mon, May 26, 2014 at 10:14 AM, Kathy Hida <[email protected]> wrote:
> Hi,
>
> Is there any implementation for clustered SVM proposed in this paper
> http://jmlr.org/proceedings/papers/v31/gu13b.pdf.
>
> The implementation seems to be trivial, as we just need to do simple
> modifications on LibLinear. I did this modifications but I still get
> results different from those shown in the paper.
>
>
>
>
>
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