SARDNET: A Self-Organizing Feature Map For Sequences (1995)
A self-organizing neural network for sequence classification called SARDNET is described and analyzed experimentally. SARDNET extends the Kohonen Feature Map architecture with activation retention and decay in order to create unique distributed response patterns for different sequences. SARDNET yields extremely dense yet descriptive representations of sequential input in very few training iterations. The network has proven successful on mapping arbitrary sequences of binary and real numbers, as well as phonemic representations of English words. Potential applications include isolated spoken word recognition and cognitive science models of sequence processing.
In G. Tesauro, D. S. Touretzky, and T. K. Leen, editors, Advances in Neural Information Processing Systems 7 (NIPS'94), 577--584, Denver, CO, 1995. Cambridge, MA: MIT Press.

Daniel L. James Undergraduate Alumni
Risto Miikkulainen Faculty risto [at] cs utexas edu