A formulation of learning vector quantization using a new misclassification measure
Atsushi Sato, Keiichi Yamada · 2002
This paper reports a formulation of learning vector quantization (LVQ) using a new misclassification measure based on minimum classification error (MCE). We show that the convergence property of reference vectors depends on the definition of the misclassification measure, and show that our definition guarantees the convergence, unlike LVQ1.1 or Juan and Katagiri's formulation based on MCE (1992). Experimental results for handwritten digit recognition reveal that the proposed method is superior to LVQ algorithms in recognition capability.