Misty K. Blowers

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This paper presents the design and implementation of an <i>adaptive open-set speaker identification system</i> with genetic learning classifier systems. One of the challenging problems in using learning classifier systems for numerical problems is the knowledge representation. The voice samples are a series of real numbers that must be encoded in a(More)
—We present the design and implementation of an open-set text-independent speaker identification system using genetic Learning Classi-fier Systems (LCS). We examine the use of this system in a real-number problem domain, where there is strong interest in its application to tactical communications. We investigate different encoding methods for representing(More)
We present a genetic classifier system approach to the text-independent open-set speaker identification problem. Classifier systems are widely used in symbolic problem for dynamically changing open-ended learning. Signal processing problems require processing of real-valued parameters that classifier systems are not designed for. On the other hand, the(More)
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