On 05/03/2014 05:37 PM, Mathieu Blondel wrote:
Same feeling as Andy. I'd favor implementing averaging instead.

I totally forgot that we don't have averaging yet ^^
I'd be in favor of geometric averaging: http://arxiv.org/abs/1212.2002



Mathieu


On Sun, May 4, 2014 at 12:23 AM, Andy <t3k...@gmail.com <mailto:t3k...@gmail.com>> wrote:

    Hi Sean.
    For linear classifiers I'm not really aware of benefits in doing
    mini-batch training, and I don't think it is widely used (someone
    correct me if I'm wrong).
    Usually we only like to add features that have a clear benefit for
    the users, to prevent scikit-learn from becoming bloated.

    Do you have a particular use-case where it is important?

    Cheers,
    Andy


    On 05/01/2014 02:04 AM, Sean Violante wrote:
    Hi

    I was wondering if there is any interest in implementing
    minibatch/batch for the SGD algorithm. As I understand it, this
    is not implemented

    "There is a compromise between the two forms, which is often
    called "mini-batches", where the true gradient is approximated by
    a sum over a small number of training examples."

    http://en.wikipedia.org/wiki/Stochastic_gradient_descent



    This would be doing a partial_fit (on small number of training
    examples) but updating the weights only after each epoch rather
    than after each training sample


    as far as I can see it would only require a flag in the
    sgd_fast.pyx code.

    thanks

    Sean


    
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