[jira] [Commented] (MAHOUT-976) Implement Multilayer Perceptron

["Suneel Marthi (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13825338#comment-13825338 ] 

Suneel Marthi commented on MAHOUT-976:
--------------------------------------

[~tdunning] There is another JIRA (see MAHOUT-1265) for a Multilayer Perceptron with Yexi's patch. Can this be marked as 'Duplicate' and closed?

> 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
>            Assignee: Ted Dunning
>            Priority: Minor
>              Labels: multilayer, networks, neural, perceptron
>             Fix For: Backlog
>
>         Attachments: MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch, MAHOUT-976.patch
>
>   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 
>  * normalization of the inputs (storeable) as part of the model
>  
> First:
>  * implementation "stocastic gradient descent" like gradient machine
>  * simple gradient descent incl. momentum
> Later (new jira issues):  
>  * Distributed Batch learning (see below)  
>  * "Stacked (Denoising) Autoencoder" - Feature Learning
>  * advanced cost minimazation like 2nd order methods, conjugate gradient etc.
> 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). 
> Batch learning with delta-bar-delta heuristics for adapting the learning rates.    
>  



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