Boltzmann Machine

Training problems: Given a set of binary data vectors, the machine must learn to predict the output vectors with high probability. The first step is to determine which layer connection weights have the lowest cost function values, relative to all the other possible binary vectors. The Boltzmann technique accomplishes this by continuously updating its own weights as each feature is processed, instead of treating the weights as a fixed value.