Daily experience shows that in the real world, the meaning of many concepts heavily depends on some implicit context, and changes in that context can cause radical changes in the concepts. This paper introduces a method for incremental concept learning in dynamic environments where the target concepts may be contextdependent and may change drastically over time. The method has been implemented in a system called FLORA3. FLORA3 is very exible in adapting to changes in the target concepts and tracking concept drift. Moreover, by explicitly storing old hypotheses and re-using them to bias learning in new contexts, it possesses the ability to utilize experience from previous learning. This greatly increases the system's e ectiveness in environments where contexts can reoccur periodically. The paper describes the various algorithms that constitute the method and reports on several experiments that demonstrate the exibility of FLORA3 in dynamic environments.
Widmer et al. REF uncovered from daily experience that the meaning of many concepts heavily depend on implicit context.
Effective Learning in Dynamic Environments by Explicit Context Tracking
European Conference on Machine Learning
Computer Science