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https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13206345#comment-13206345
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Ted Dunning commented on MAHOUT-976:
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I would leave classify as it stands. If the user wants that sort of function,
it can be very convenient. As you point out, classifyFull is probably more
useful for lots of applications.
Why do you need the flag? Why not just let the user decide how to use their
model?
> 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
> * simple gradient descent
> Later (new jira issues):
> * momentum for better and faster learning
> * advanced cost minimazation like 2nd order methods, conjugate gradient etc.
>
> * 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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