A stop criterion for the Boltzmann machine learning algorithm.
Berthold Ruf · 1994
: Ackley, Hinton and Sejnowski introduced a very interesting and versatile learning algorithm for the Boltzmann machine (BM). However it is difficult to decide when to stop the learning procedure. Experiments have shown that the BM may destroy previously achieved results when the learning process is executed for too long. This paper introduces a new quantity, the conditional divergence, measuring the learning success for the inputs of the data set. To demonstrate its use, some experiments are presented, based on the Encoder Problem. 1 Introduction The Boltzmann machine (BM), introduced by Ackley, Hinton and Sejnowski in [Ack 84] is one of the most interesting neural networks. This paper first summarizes the basic concepts of the BM and gives in chapter 2 a short description of the learning algorithm, which was also introduced in [Ack 84]. Chapter 3 analyzes the convergence behavior of the algorithm and introduces a new quantity which makes it possible to decide when to stop th...