Boltzmann machines [122] are a network of symmetrically connected, neuron-like units, which are used for stochastic decisions on the given datasets. Initially, they were introduced to learn the probability distributions over binary vectors. Boltzmann machines possess a simple learning algorithm, which helps them to infer and reach interesting conclusions about input datasets containing binary vectors. The learning algorithm becomes very slow in networks with many layers of feature detectors; however, with one layer of feature detector at a time, learning can be much faster.
To solve a learning problem, Boltzmann machines consist of a set of binary data vectors, and update the weight on the respective connections so that the data vectors turn out to be good solutions for the optimization problem laid by the weights. The Boltzmann machine, to solve the learning problem, makes lots of small updates to these weights.
The Boltzmann machine over a d-dimensional binary vector can...
