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

["Viktor Gal (Commented) (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MAHOUT-976?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13202759#comment-13202759 ] 

Viktor Gal commented on MAHOUT-976:
-----------------------------------

Although it's not the same (but again a NN) and afaik the learning is sequential, but it's worth to check out the restricted boltzmann machine implementation that has been just submitted to MAHOUT-968
                
> 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 in batch learning by:
>  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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