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Initialization insensitive LVQ algorithm based on cost-function adaptation
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DOI：10.1016/j.patcog.2004.11.011.png)
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Abstract
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A learning vector quantization (LVQ) algorithm called harmonic to minimum LVQ algorithm (H2M-LVQ)(1) is presented to tackle the initialization sensitiveness problem associated with the original generalized LVQ (GLVQ) algorithm. Experimental results show superior performance of the H2M-LVQ algorithm over the GLVQ and one of its variants on several datasets. (C) 2005 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
Keywords:
generalized learning vector quantization
harmonic average distance
initialization sensitiveness
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