Adaptive and On-line Learning in Non-Stationary Environments, Evolving Systems

This special issue aims at discussing novel efficient techniques, methods and tools in these directions in order to be able to manage, to exploit and to interpret correctly the increasing amount of data in environments that are con - tinuously changing. It thus includes some recent methods going beyond state-of-the-art and addressing the advances and challenges of learning in non-stationary environments. In particular, it handles several real-world applications that require on-line and evolving learning capabilities which have been hardly tackled before in this context.