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https://issues.apache.org/jira/browse/MAHOUT-1551?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14060688#comment-14060688
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Felix Schüler commented on MAHOUT-1551:
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Ted, thanks for the feedback!
As far as we understand it, the implementation is a simple online/stochastic
gradient descent using backpropagation to calculate the gradients of the error
function. Weights are then updated with a fixed learning rate that never
changes. As we (I always say 'we' because I am working on it with someone else
for a university-class) have described in MAHOUT-1388, the CLI version only
performs a fixed number of n iterations where n is the size of the training
set. So example is fed into the network once, which in case of a dataset as
small as the iris dataset does not lead to acceptable performance. The unit
test for the mlp iterates 2000 times through the dataset to achieve a good
performance, but as far as we can tell, stopping does not depend on learning or
weight updates even though regularization is implemented.
We could add this information to the implementation section of the
documentation.
As for the DSL, we are very tempted to implement the MLP or a more general
neural network framework. We will think about it and see if we can find the
time.
> Add document to describe how to use mlp with command line
> ---------------------------------------------------------
>
> Key: MAHOUT-1551
> URL: https://issues.apache.org/jira/browse/MAHOUT-1551
> Project: Mahout
> Issue Type: Documentation
> Components: Classification, CLI, Documentation
> Affects Versions: 0.9
> Reporter: Yexi Jiang
> Labels: documentation
> Fix For: 1.0
>
> Attachments: README.md
>
>
> Add documentation about the usage of multi-layer perceptron in command line.
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