A neural associative pattern classifier

In this work, we study the behaviour of the Bidirectional Associative Memory (BAM) in terms of the supporting neural structure, with a view to its possible improvements as a useful Pattern Classifier by means of class associations from unknown inputs, once mentioned classes have been previously defined by one or even more prototypes. The best results have been obtained by suitably choosing the training pattern pairs, the thresholds, and the activation functions of the network's neurones, by means of certain proposed methods described in the paper. In order to put forward the advantages of these proposed methods, the classifier has been applied on an especially popular hand-written character set as the well-known NIST#19 character database, and with one of the UCI's data bases. In all cases, the method led to a marked improvement in the performance achievable by a BAM, with a 0% error rate.