A structural learning algorithm with forgetting of link weights

Summary form only given, as follows. Backpropagation learning suffers from serious drawbacks: first the necessity of a priori specification of a model structure, and second the difficulty in interpreting hidden units. To cope with these drawbacks the author proposes a novel learning algorithm, called structural learning algorithm, which generates a skeletal structure of a network: a network in which minimum number of links and a minimum number of hidden units are actually used. The resulting skeletal structure solves the first difficulty of trial and error. It also solves the second difficulty due to its clarity. In addition to these two benefits, the structural learning algorithm is also advantageous in dealing with a network composed of multiple modules. It explains how links from other modules emerge, while pruning those from the outside world full of redundant information.<>