| Popis: |
We present connectionist classifiers that form distributed low-level knowledge representations for pattern recognition, given random feature vectors generated from dual statistically distinct sources. The classifier is a functional representation of an oscillatory network consisting of two-member clusters of model neurons whose efferent synapses may be either inhibitory or excitatory. We demonstrate the oscillatory network's performance in the context of source- dependent single speaker identification. In these tests, the backpropagation network representation learning curve began to flatten around an unacceptable error response. The oscillatory model, however, was able to discriminate accurately.© (1993) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. |
