A novel neural hetero-associative memory model for pattern recognition
Pattern Recognition
A new learning strategy of general BAMs
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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Necessary and sufficient conditions are derived for the weights of a generalized correlation matrix of a bidirectional associative memory (BAM) which guarantee the recall of all training pairs. A linear programming/multiple training (LP/MT) method that determines weights which satisfy the conditions when a solution is feasible is presented. The sequential multiple training (SMT) method is shown to yield integers for the weights, which are multiplicities of the training pairs. Computer simulation results, including capacity comparisons of BAM, LP/MT BAM, and SMT BAM, are presented