Boltzmann machines also use symmetric weights, but include units that are neither input nor output units .
Boltzmann machine
The Boltzmann machine can be thought of as a noisy Hopfield network.
The first step involves training a series of simple 2-layer networks with hidden layer units which are not connected to one another (known as "restricted Boltzmann machines", or RBMs) to reproduce its own training images.
• Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics.
A pretraining technique developed by Geoffrey Hinton for training many-layered "deep" auto-encoders involves treating each neighboring set of two layers like a Restricted Boltzmann Machine for pre-training to approximate a good solution and then ...
There are many types of networks - ranging from simple boolean networks (Perceptrons), to complex self-organizing networks (Kohonen networks), to networks modelling thermodynamic properties (Boltzmann machines)! ...
The color values in an image or the temperature across an object can be modeled as Markov random fields. When the variables are binary, this model is a Boltzmann machine. See Cressie, Li, Hertz (ch 7), Kindermann, and Relaxation labeling.
 See also: Neural network, Machine learning, Perceptron, Percept, Classification
  
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