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From "Hudson (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (HAMA-795) Implement Autoencoder based on NeuralNetwork
Date Wed, 28 Aug 2013 03:43:51 GMT

    [ https://issues.apache.org/jira/browse/HAMA-795?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13752078#comment-13752078
] 

Hudson commented on HAMA-795:
-----------------------------

SUCCESS: Integrated in Hama trunk #160 (See [https://builds.apache.org/job/Hama%20trunk/160/])
HAMA-795: Implement Autoencoder based on NeuralNetwork (yxjiang: rev 1518055)
* /hama/trunk/CHANGES.txt
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/AbstractLayeredNeuralNetwork.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/AutoEncoder.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetwork.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/ann/SmallLayeredNeuralNetworkTrainer.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/SmallMLPTrainer.java
* /hama/trunk/ml/src/test/java/org/apache/hama/ml/MLTestBase.java
* /hama/trunk/ml/src/test/java/org/apache/hama/ml/ann/TestAutoEncoder.java
* /hama/trunk/ml/src/test/java/org/apache/hama/ml/ann/TestSmallLayeredNeuralNetwork.java
* /hama/trunk/ml/src/test/java/org/apache/hama/ml/ann/TestSmallLayeredNeuralNetworkMessage.java
* /hama/trunk/ml/src/test/resources/dimensional_reduction.txt

                
> Implement Autoencoder based on NeuralNetwork
> --------------------------------------------
>
>                 Key: HAMA-795
>                 URL: https://issues.apache.org/jira/browse/HAMA-795
>             Project: Hama
>          Issue Type: New Feature
>          Components: machine learning
>    Affects Versions: 0.6.3
>            Reporter: Yexi Jiang
>            Assignee: Yexi Jiang
>         Attachments: HAMA-795.patch, HAMA-795.patch
>
>
> Implement the autoencoder based on the NeuralNetwork (Implemented in Hama-770).
> The autoencoder can be used for dimensional reduction (non-linear) and the building block
of deep learning network.

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