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This paper presents a discriminative training approach to irrelevant variability normalization (IVN) based joint training of feature transforms and prototype-based classifier for recognition of online handwritten Chinese characters. A sample separation margin based minimum classification error criterion is adopted in IVN-based training, while an Rprop algorithm is used for optimizing the objective...
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...
We present a study of designing compact recognizers of handwritten Chinese characters using multiple-prototype based classifiers. A modified Quick prop algorithm is proposed to optimize a sample-separation-margin based minimum classification error objective function. Split vector quantization technique is used to compress classifier parameters. Benchmark results are reported for classifiers with different...
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