[MAHOUT-968] Classifier based on restricted boltzmann machines

• Type: New Feature
• Status: Closed
• Priority: Major
• Resolution: Won't Fix
• Affects Version/s: 0.7
• Fix Version/s: 0.10.0
• Labels: classification mnist
• Assignee: Robin Anil
• Reporter: Dirk Weißenborn
• Created: 01/Feb/12 11:31
• Updated: 31/Jan/24 22:12
• Resolved: 08/Mar/14 12:04

Description

This is a proposal for a new classifier based on restricted boltzmann machines. The development of this feature follows the paper on "Deep Boltzmann Machines" (DBM) [1] from 2009. The proposed model (DBM) got an error rate of 0.95% on the mnist dataset [2], which is really good. Main parts of the implementation should also be applicable to other scenarios than classification where restricted boltzmann machines are used (ref. MAHOUT-375).
I am working on this feature right now, and the results are promising. The only problem with the training algorithm is, that it is still mostly sequential (if training batches are small, what they should be), which makes Map/Reduce until now, not really beneficial. However, since the algorithm itself is fast (for a training algorithm), training can be done on a single machine in managable time.
Testing of the algorithm is currently done on the mnist dataset itself to reproduce results of [1]. As soon as results indicate, that everything is working fine, I will upload the patch.

[1] http://www.cs.toronto.edu/~hinton/absps/dbm.pdf
[2] http://yann.lecun.com/exdb/mnist/