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We present a study of designing compact multiple-prototype based classifiers for rotation-free recognition of online handwritten Chinese characters. Several versions of Rprop algorithms are adopted to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters and a fast-match tree is used...
In recent years, various discriminative learning techniques for HMMs have consistently yielded significant benefits in speech recognition. In this paper, we present a novel optimization technique using the minimum classification error (MCE) criterion to optimize the HMM parameters. Unlike maximum mutual information training where an extended Baum-Welch (EBW) algorithm exists to optimize its objective...
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