Skip to Main content Skip to Navigation
Conference papers

Man-Machine Cooperation for the On-Line Training of an Evolving Classifier

Abstract : Touch sensitive interfaces enable new interaction methods, like using gesture commands. To easily memorize more than a dozen of gesture commands, it is important to be able to customize them. The classifier used to recognize drawn symbols must hence be customizable, able to learn from very few data, and evolving, able to learn and improve during its use. This work studies the importance and the impact of using reject to supervise the on-line training of the evolving classifier. The objective is to obtain a gesture command system that cooperates as best as possible with the user: to learn from its mistakes without soliciting him too often. There is a trade-off between the number of user interactions, to supervise the on-line learning, and the number of classification errors, that require a correction from the user.
Document type :
Conference papers
Complete list of metadata

Cited literature [14 references]  Display  Hide  Download
Contributor : Manuel Bouillon Connect in order to contact the contributor
Submitted on : Monday, March 31, 2014 - 4:58:09 PM
Last modification on : Tuesday, October 19, 2021 - 11:58:55 PM
Long-term archiving on: : Monday, April 10, 2017 - 7:34:13 AM


Files produced by the author(s)



Manuel Bouillon, Eric Anquetil. Man-Machine Cooperation for the On-Line Training of an Evolving Classifier. IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS), Jun 2014, Linz, Austria. pp.1-7, ⟨10.1109/EAIS.2014.6867477⟩. ⟨hal-00968257⟩



Record views


Files downloads