Learning algorithms for Boltzmann machines

The author describes a learning algorithm for Boltzmann machines, based on the usual alternation between `learning´ and `hallucinating´ phases. He outlines the rigorous proof that, for suitable choices of the parameters, the evolution of the weights follows very closely, with very high probability, an integral trajectory of the gradient of the likelihood function whose global maxima are exactly the desired weight patterns