Thanks Olivier! I really appreciate your updates on these issues!

As far as I can tell Averaged SGD is similar to the Averaged
Perceptron in that you simply average the weight vectors after each
iteration (i.e. training sample). Of course, this can be done very
efficiently in constant time and memory. AFAIK you cannot use this
strategy for L1 regularization, tough.

The results in [Xu 2011] are pretty impressive given the simplicity of
the algorithm - we should definitely give it a try. Unfortunately, the
algorithm shares some of the undesirable properties of SGD: you need a
number of heuristics to make it work (e.g. learning rate schedule,
averaging start point t_0)

best,
 Peter

[Xu 2011] http://arxiv.org/pdf/1107.2490v1

2011/10/11 Olivier Grisel <[email protected]>:
> I think people here (e.g. @pprett) might be interested in the
> following new release of Léon Bottou's influential project:
>
>  http://leon.bottou.org/projects/sgd
>
> It did not know about Averaged SGD. Will have to read the cited references.
>
> I wonder if those results are transposable on online clustering and /
> or online matrix factorization.
>
> --
> Olivier
> http://twitter.com/ogrisel - http://github.com/ogrisel
>
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-- 
Peter Prettenhofer

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threats, fraudulent activity and more. Splunk takes this data and makes
sense of it. Business sense. IT sense. Common sense.
http://p.sf.net/sfu/splunk-d2d-oct
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