Hello Ted, I would have to study the paper you ve given me first a little bit. What I could do at the moment is a small adn easy overview over the model and algorithm I am implementing... Deep Boltzmann Machines that I am using for classification are artificial neural networks based on stacked restricted boltzmann machines. I think the models are quite different on how they exactly work since the model of the paper you wrote isn t an artificial neural network or anything close at the first glance. Therefore it seems to me that comparing the algorithms is quite difficult. If you still would like a comparison, I will see what I can do.
regards Dirk 2012/2/1 Ted Dunning <[email protected]> > Dirk, > > Can you provide some comparison with RBM's and the Bayesian learning > algorithm such as described here: > http://research.microsoft.com/apps/pubs/default.aspx?id=122779 > > On Wed, Feb 1, 2012 at 3:32 AM, Dirk Weißenborn (Created) (JIRA) < > [email protected]> wrote: > > > Classifier based on restricted boltzmann machines > > ------------------------------------------------- > > > > Key: MAHOUT-968 > > URL: https://issues.apache.org/jira/browse/MAHOUT-968 > > Project: Mahout > > Issue Type: New Feature > > Components: Classification > > Reporter: Dirk Weißenborn > > > > > > 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/ > > > > -- > > This message is automatically generated by JIRA. > > If you think it was sent incorrectly, please contact your JIRA > > administrators: > > https://issues.apache.org/jira/secure/ContactAdministrators!default.jspa > > For more information on JIRA, see: > http://www.atlassian.com/software/jira > > > > > > >
