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Miguel Melgarejo

Andrea Villate

David Rincon

Abstract

This paper presents a methodologicalapproach for tuning fuzzy classifiersintended to recognize the Australiansign-language considering twoparticular contexts. We describe thefuzzy classification architecture andthe tuning process based on differentialevolution. The validation resultsshow that it is possible to find a fuzzyclassifier whose classification error isaround 13.0% over a group of wordstaken from several experts for eachinteraction context. This characteristicis relevant as previous works only consideredrecognizing words providedonly by one interpreter.

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Keywords

Auslan, differential evolution, fuzzy classification, pattern recognition, sign language, optimization, TSK Fuzzy Systems

References
How to Cite
Melgarejo, M., Villate, A., & Rincon, D. (2012). Applying Differential Evolution to Tune Fuzzy Classifiers Intended for Sign-Language Recognition. Ingenieria Y Universidad, 16(2), 397. Retrieved from https://revistas.javeriana.edu.co/index.php/iyu/article/view/1691
Section
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