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https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13205471#comment-13205471
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Christian Herta commented on MAHOUT-976:
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The AbstractVectorClassifier Method classify(..) assumes that in general there
are n mutually exclusive classes. This is also the standard characteristics of
the convenience function classifyFull(..). For a Multilayer Perceptron this is
not necessary the case. In the current work-in -progress implementation this
will be configured in the constructor of the MLP by a boolean
"mutuallyExclusiveClasses".
I could overwrite classifyFull and throw a UnsupportedOperationException() if
classify is used for "mutuallyExclusiveClasses = false". But I assume that
would be confusing for the user.
Is there a better solution?
> Implement Multilayer Perceptron
> -------------------------------
>
> Key: MAHOUT-976
> URL: https://issues.apache.org/jira/browse/MAHOUT-976
> Project: Mahout
> Issue Type: New Feature
> Affects Versions: 0.7
> Reporter: Christian Herta
> Priority: Minor
> Labels: multilayer, networks, neural, perceptron
> Original Estimate: 80h
> Remaining Estimate: 80h
>
> Implement a multi layer perceptron
> * via Matrix Multiplication
> * Learning by Backpropagation; implementing tricks by Yann LeCun et al.:
> "Efficent Backprop"
> * arbitrary number of hidden layers (also 0 - just the linear model)
> * connection between proximate layers only
> * different cost and activation functions (different activation function in
> each layer)
> * test of backprop by gradient checking
>
> First:
> * implementation "stocastic gradient descent" like gradient machine
> Later (new jira issues):
> * Distributed Batch learning (see below)
> * "Stacked (Denoising) Autoencoder" - Feature Learning
>
> Distribution of learning can be done by (batch learning):
> 1 Partioning of the data in x chunks
> 2 Learning the weight changes as matrices in each chunk
> 3 Combining the matrixes and update of the weights - back to 2
> Maybe this procedure can be done with random parts of the chunks (distributed
> quasi online learning)
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