Classification using Discriminative Restricted Boltzmann Machines

author: Hugo Larochelle, University of Montreal
published: Aug. 7, 2008,   recorded: July 2008,   views: 1072
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Description

Recently, many applications for Restricted Boltzmann Machines (RBMs) have been developed for a large variety of learning problems. However, RBMs are usually used as feature extractors for another learning algorithm or to provide a good initialization for deep feed-forward neural network classifiers, and are not considered as a stand-alone solution to classification problems. In this paper, we argue that RBMs provide a self-contained framework for deriving competitive non-linear classifiers. We present an evaluation of different learning algorithms for RBMs which aim at introducing a discriminative component to RBM training and improve their performance as classifiers. This approach is simple in that RBMs are used directly to build a classifier, rather than as a stepping stone. Finally, we demonstrate how discriminative RBMs can also be successfully employed in a semi-supervised setting.

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Reviews and comments:

Comment1 abc, May 27, 2013 at 8:17 p.m.:

To whomever is recording these videos - pls only focus video on the presenter's slides. We do not need to look at the presenter, while not knowing what he is referring to on the screen - especially, when the slides are not available... Thnx

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