|Title||Learning in Nonstationary Environments: A Hybrid Approach|
|Publication Type||Conference Paper|
|Year of Publication||2017|
|Authors||Alippi, C., W. Qi, and M. Roveri|
|Editor||Rutkowski, L., M. Korytkowski, R. Scherer, R. Tadeusiewicz, L. A. Zadeh, and J. M. Zurada|
|Conference Name||Artificial Intelligence and Soft Computing|
|Publisher||Springer International Publishing|
Solutions present in the literature to learn in nonstationary environments can be grouped into two main families: passive and active. Passive solutions rely on a continuous adaptation of the envisaged learning system, while the active ones trigger the adaptation only when needed. Passive and active solutions are somehow complementary and one should be preferred than the other depending on the nonstationarity rate and the tolerable computational complexity. The aim of this paper is to introduce a novel hybrid approach that jointly uses an adaptation mechanism (as in passive solutions) and a change detection triggering the need to retrain the learning system (as in active solutions).