A Bidirectional Hetero-Associative Memory for True-Color Patterns
Neural Processing Letters
A New Associative Model with Dynamical Synapses
Neural Processing Letters
A model of an intraconnected neural parallel bidirectional memory
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A minimum interconnection direct storage model of a neural bidirectional memory
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Comparison of an efficient direct storage BAM model with traditional neural bidirectional memories
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Large-scale pattern storage and retrieval using generalized brain-state-in-a-box neural networks
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
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The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in this way are evidenced, like the fact that surprisingly they follow a generalized Hebb's law. The performance of the SVM approach is compared to existing methods with nonsymmetric connections, by some design examples