SARDNET: A Self-Organizing Feature Map for Sequences
Daniel L. James, Risto P Miikkulainen · 1994
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. 1 INTRODUCTION While neural networks have proved a good tool for processing static patterns, classifying sequential information has remained a challenging task. The problem involves recognizing patterns in a time series of vectors, which requires forming a good internal representation for the sequences. Several researchers have p...