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https://issues.apache.org/jira/browse/MAHOUT-228?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12900200#action_12900200
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Ted Dunning commented on MAHOUT-228:
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OK.
I think that was the final commit of the basic M-228 functionality. The
results should appear in
https://hudson.apache.org/hudson/job/Mahout-Quality/204/
We now have a reasonable selection of alternatives for online vector learning.
This includes versions that do online cross-validation with AUC and a version
that does hyper-parameter selection and annealing using evolutionary techniques
on top of the on-line cross-validation version.
I will be adding test cases over the next few weeks, but after the dust clears
here, I will be closing M-228. Jake should be proud.
> Need sequential logistic regression implementation using SGD techniques
> -----------------------------------------------------------------------
>
> Key: MAHOUT-228
> URL: https://issues.apache.org/jira/browse/MAHOUT-228
> Project: Mahout
> Issue Type: New Feature
> Components: Classification
> Reporter: Ted Dunning
> Assignee: Ted Dunning
> Fix For: 0.4
>
> Attachments: logP.csv, MAHOUT-228-3.patch,
> MAHOUT-228-interfaces.patch, MAHOUT-228.patch, MAHOUT-228.patch,
> MAHOUT-228.patch, MAHOUT-228.patch, r.csv, sgd-derivation.pdf,
> sgd-derivation.tex, sgd.csv, TrainLogisticTest.patch
>
>
> Stochastic gradient descent (SGD) is often fast enough for highly scalable
> learning (see Vowpal Wabbit, http://hunch.net/~vw/).
> I often need to have a logistic regression in Java as well, so that is a
> reasonable place to start.
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