DocumentCode :
2506864
Title :
Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems
Author :
Almaksour, Abdullah ; Anquetil, Eric ; Quiniou, Solen ; Cheriet, Mohamed
Author_Institution :
INSA de Rennes, Rennes, France
fYear :
2010
fDate :
23-26 Aug. 2010
Firstpage :
4056
Lastpage :
4059
Abstract :
In this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a fuzzy adaptation method in order to learn and maintain the model. We use this method to build an evolving handwritten gesture recognition system. The self-adaptive nature of this system allows it to start its learning process with few learning data, to continuously adapt and evolve according to any new data, and to remain robust when introducing a new unseen class at any moment in the life-long learning process.
Keywords :
fuzzy set theory; gesture recognition; handwritten character recognition; pattern classification; pattern clustering; customizable self-evolving fuzzy rule-based classifier; evolving handwritten gesture recognition system; fuzzy adaptation method; fuzzy classifier; incremental clustering algorithm; life-long learning process; Artificial neural networks; Clustering algorithms; Computational modeling; Covariance matrix; Gesture recognition; Prototypes; Robustness; evolving; fuzzy classifier; handwriting recognition; incremental learning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition (ICPR), 2010 20th International Conference on
Conference_Location :
Istanbul
ISSN :
1051-4651
Print_ISBN :
978-1-4244-7542-1
Type :
conf
DOI :
10.1109/ICPR.2010.986
Filename :
5597395
Link To Document :
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