Continuous marking menus for learning cursive pen-based gestures
Proceedings of the 16th international conference on Intelligent user interfaces
HBF49 feature set: A first unified baseline for online symbol recognition
Pattern Recognition
| Hi-index | 0.00 |
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.