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From Dirk Weißenborn (Updated) (JIRA) <j...@apache.org>
Subject [jira] [Updated] (MAHOUT-968) Classifier based on restricted boltzmann machines
Date Tue, 07 Feb 2012 19:18:59 GMT

     [ https://issues.apache.org/jira/browse/MAHOUT-968?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
]

Dirk Weißenborn updated MAHOUT-968:
-----------------------------------

    Status: Patch Available  (was: Open)

initial patch submitted 
                
> Classifier based on restricted boltzmann machines
> -------------------------------------------------
>
>                 Key: MAHOUT-968
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-968
>             Project: Mahout
>          Issue Type: New Feature
>          Components: Classification
>    Affects Versions: 0.7
>            Reporter: Dirk Weißenborn
>              Labels: classification, mnist
>             Fix For: 0.7
>
>         Attachments: MAHOUT-968.patch
>
>   Original Estimate: 336h
>  Remaining Estimate: 336h
>
> This is a proposal for a new classifier based on restricted boltzmann machines. The development
of this feature follows the paper on "Deep Boltzmann Machines" (DBM) [1] from 2009. The proposed
model (DBM) got an error rate of 0.95% on the mnist dataset [2], which is really good. Main
parts of the implementation should also be applicable to other scenarios than classification
where restricted boltzmann machines are used (ref. MAHOUT-375).
> I am working on this feature right now, and the results are promising. The only problem
with the training algorithm is, that it is still mostly sequential (if training batches are
small, what they should be), which makes Map/Reduce until now, not really beneficial. However,
since the algorithm itself is fast (for a training algorithm), training can be done on a single
machine in managable time.
> Testing of the algorithm is currently done on the mnist dataset itself to reproduce results
of [1]. As soon as results indicate, that everything is working fine, I will upload the patch.
> [1] http://www.cs.toronto.edu/~hinton/absps/dbm.pdf
> [2] http://yann.lecun.com/exdb/mnist/

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