Title: Classification using discriminative restricted Boltzmann machines
Abstract: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 learn...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 standalone 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.Read More
Publication Year: 2008
Publication Date: 2008-01-01
Language: en
Type: article
Indexed In: ['crossref']
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Cited By Count: 663
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