Space: Apache Mahout (https://cwiki.apache.org/confluence/display/MAHOUT)
Page: Restricted Boltzmann Machines 
(https://cwiki.apache.org/confluence/display/MAHOUT/Restricted+Boltzmann+Machines)


Edited by Sisir Koppaka:
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NOTE: This implementation is a Work-In-Progress, at least till September, 2010. 

The JIRA issue is [here|https://issues.apache.org/jira/browse/MAHOUT-375]. 

h3. Boltzmann Machines
Boltzmann Machines are a type of stochastic neural networks that closely 
resemble physical processes. They define a network of units with an overall 
energy that is evolved over a period of time, until it reaches thermal 
equilibrium. 

However, the convergence speed of Boltzmann machines that have unconstrained 
connectivity is low.

h3. Restricted Boltzmann Machines
Restricted Boltzmann Machines are a variant, that are 'restricted' in the sense 
that connections between hidden units of a single layer are _not_ allowed. In 
addition, stacking multiple RBM's is also feasible, with the activities of the 
hidden units forming the base for a higher-level RBM. The combination of these 
two features renders RBM's highly usable for parallelization. 

In the Netflix Prize, RBM's offered distinctly orthogonal predictions to SVD 
and k-NN approaches, and contributed immensely to the final solution.

h3. RBM's in Apache Mahout
An implementation of Restricted Boltzmann Machines is being developed for 
Apache Mahout as a Google Summer of Code 2010 project. The key aims of the 
implementation are:
#Accurate - should replicate known results, including those of the Netflix Prize
#Fast - The implementation uses Map-Reduce, hence, it should be fast
#Scale - Should scale to large datasets, with a design whose critical parts 
don't need a dependency between the amount of memory on your cluster systems 
and the size of your dataset

You can view the patch as it develops 
[here|http://github.com/sisirkoppaka/mahout-rbm/compare/trunk...rbm].

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