The Boltzmann machine

Boltzmann machines are discrete networks with input, output and hidden units, in which the units update their state with a stochastic function. The output of a given node is calculated using probabilities, rather than threshold or sigmoid function. The paper describes their basic architecture, their processing mechanism, the principles of their operation, and their learning mechanism. The main characteristics of the Boltzmann machine is the fact that, when subjected to reducing noise, it has a final probability of resting in given states which is in direct proportion to Hopfield's calculation of the energy of those states. The key problem is to control the values of such energies by changing the weights.