Hello,
The Perceptron can be seen as a SGD algorithm optimizing the loss \sum_i
max{t - y_i w^T x_i, 0} where t=0. On the other hand, online SVM optimizes
the same loss but with t=1 (the advantage of setting t=1 rather than t=0 is
that it then upper-bounds the zero-one loss).
You can check that our implementation behaves correctly here:
https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/linear_model/tests/test_perceptron.py
The advantage of our implementation is that it benefits of all the effort
we have already put into SGD.
Mathieu
On Thu, Sep 6, 2012 at 8:46 PM, Jaidev Deshpande <deshpande.jai...@gmail.com
> wrote:
> Hello,
>
> I've been playing around with the Perceptron class in scikit-learn. I
> have a theoretical understanding of the perceptron algorithm. In
> sklearn it has been subclassed from the SGDClassifier class, very
> different from how I would have expected the perceptron to be
> implemented (I'd have thought it was simply something like this -
> http://en.wikipedia.org/wiki/Perceptron#Learning_algorithm_steps).
>
> Anyhow, I have some questions about the methods used in the
> SGDClassifier class. Would it be better if I asked them here on the
> list or should I comment on the code in github? I would prefer the
> latter, since I have a feeling that the discussion might grow long.
>
> Thanks
>
>
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