Analysis and Comparison of Different Learning Algorithms for Pattern Association Problems

As test cases we use simple pat(cid:173) tern association problems, such as the XOR-problem and symmetry de(cid:173) tection problems. The algorithms considered are either versions of the Boltzmann machine learning rule or based on the backpropagation of errors. We also propose and analyze a generalized delta rule for linear threshold units. We find that the performance of a given learning algorithm depends strongly on the type of units used. In particular, we observe that networks with 1 units quite generally exhibit a significantly better learning behavior than the correspon(cid:173) ding 0,1 versions.