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Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems

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Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems
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-incremental learning; evolving; handwriting recognition; fuzzy classifier;
Abdullah Almaksour, Eric Anquetil, Solen Quiniou,
Added 09 Aug 2010
Updated 09 Aug 2010
Type Conference
Year 2010
Where ICPR
Authors Abdullah Almaksour, Eric Anquetil, Solen Quiniou, Mohammed Cheriet
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