Man-machine cooperation for the on-line training of an evolving classifier

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.