A systematic approach to classifier selection on combining multiple classifiers for handwritten digit recognition.
Resource URI: https://dblp.l3s.de/d2r/resource/publications/conf/icdar/KimSC97
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1997
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A systematic approach to classifier selection on combining multiple classifiers for handwritten digit recognition.
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character recognition; classifier selection method; multiple classifiers; handwritten digit recognition; handwritten character recognition; optimal combination results; inter-classifier dependency; similarity measure; error generation; performance comparison; gradient feature set; structural feature set; up-down left-right hole feature set; mesh feature set; large stroke feature set; combination methods; majority voting; Borda count; linear confidence accumulation; recognition rate; neural net classifiers
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A systematic approach to classifier selection on combining multiple classifiers for handwritten digit recognition.
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