A study of a new misclassification measure for minimum classification error training of prototype-based pattern classifiers

Abstract

In this paper, we revisit the formulation of minimum classification error (MCE) training and propose a sample separation margin (SSM) based misclassification measure for MCE training of multiple-prototype-based pattern classifiers. Comparative experiments are conducted on the task of the recognition of isolated online handwritten Japanese Kanji characters using Nakayosi and Kuchibue databases. Experimental results demonstrate that MCE training with the new misclassification measure achieves significant character recognition error rate reduction compared with MCE training using two traditional misclassification measures.