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Classification using Discriminative Restricted Boltzmann Machines

Classification using Discriminative Restricted Boltzmann Machines

This video was recorded at 25th International Conference on Machine Learning (ICML), Helsinki 2008. 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... Show More

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