A restricted Boltzmann machine (RBM) is a form of Boltzmann Machine where input and output layers are fully connected to each another, but have no internal connections. That is there are input-ouptut connections, but neither input-input, nor output-output links. Otherwise they are like a standard Boltzmann Machine with a training phase with inputs clamped and outputs free to vary and then a recall mode where some inputs are clamped and the network fills in the gaps.
As well as this reconstruction mode, RBMs are often used for feature detection in the early stages of deep neural networks as they use feedforward learning rather than backpropagation.
The restricted Boltzmann machine, like the Boltzmann machine, is a form of neural network and autoencoder.
Also known as: RBM
Used in glossary entries: autoencoder, backpropagation, Boltzmann machine, deep neural network, neural network
Links:
Wikipedia: Restricted Boltzmann machine
Restricted Boltzmann Machine