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https://issues.apache.org/jira/browse/HAMA-760?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13680027#comment-13680027
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Hudson commented on HAMA-760:
-----------------------------
Integrated in Hama-Nightly #937 (See
[https://builds.apache.org/job/Hama-Nightly/937/])
HAMA-760: Add new features to existing Multi Layer Perceptron (Yexi Jiang
via edwardyoon) (Revision 1491624)
Result = SUCCESS
edwardyoon :
Files :
* /hama/trunk/CHANGES.txt
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/CostFunction.java
*
/hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/CostFunctionFactory.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/CrossEntropy.java
*
/hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/MultiLayerPerceptron.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/Sigmoid.java
*
/hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/SmallMLPMessage.java
*
/hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/SmallMLPTrainer.java
*
/hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/SmallMultiLayerPerceptron.java
* /hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/SquaredError.java
*
/hama/trunk/ml/src/main/java/org/apache/hama/ml/perception/SquashingFunctionFactory.java
*
/hama/trunk/ml/src/test/java/org/apache/hama/ml/perception/TestSmallMLPMessage.java
*
/hama/trunk/ml/src/test/java/org/apache/hama/ml/perception/TestSmallMultiLayerPerceptron.java
> Add new features to existing Multi Layer Perceptron
> ---------------------------------------------------
>
> Key: HAMA-760
> URL: https://issues.apache.org/jira/browse/HAMA-760
> Project: Hama
> Issue Type: New Feature
> Reporter: Yexi Jiang
> Assignee: Yexi Jiang
> Labels: features, machine_learning, mlp
> Fix For: 0.6.2
>
> Attachments: HAMA-760.patch, HAMA-760.patch
>
>
> Current MultiLayerPerceptron has only implemented the basic features of a
> Multi Layer Perceptron.
> There are still several features need to be implemented.
> In the next step the following features should be added:
> 1) add more cost functions such as cross entropy.
> 2) add momentum and regularization.
> 3) make the training method in MLP be public to allow user to use MLP in a
> standalone algorithm.
> 4) add more test cases on other applications, at least one with regression
> and one with classification.
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