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

Abdullah Almaksour 1, * Eric Anquetil 1 Solen Quiniou 2 Mohamed Cheriet 2
* Corresponding author
1 IMADOC - Interprétation et Reconnaissance d’Images et de Documents
UR1 - Université de Rennes 1, INSA Rennes - Institut National des Sciences Appliquées - Rennes, CNRS - Centre National de la Recherche Scientifique : UMR6074
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.
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https://hal.archives-ouvertes.fr/hal-00582438
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  • HAL Id : hal-00582438, version 1

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Abdullah Almaksour, Eric Anquetil, Solen Quiniou, Mohamed Cheriet. Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems. International Conference on Pattern Recognition (ICPR), Aug 2010, Istanbul, Turkey. pp.4056-4059. ⟨hal-00582438⟩

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