@article {pub3237,
	title = {Incremental On-line learning: A review and comparison of state of the art algorithms},
	author = {Viktor Losing AND Barbara Hammer AND Heiko Wersing},
	year = {2017},
	abstract = {Recently, incremental and on-line learning gained more attention especially in
the context of big data and learning from data streams, conflicting with the
traditional assumption of complete data availability. Even though a variety of
different methods are available, it often remains unclear which of them is suit-
able for a specific task and how they perform in comparison to each other.
We analyze the key properties of seven popular incremental methods represent-
ing different algorithm classes. Thereby, we evaluate them with regards to their
on-line classification error as well as to their behavior in the limit. Further, we
discuss the often neglected issue of hyperparameter optimization specifically for
each method and test how robustly it can be done based on a small set of exam-
ples. Our extensive evaluation on data sets with different characteristics gives
an overview of the performance with respect to accuracy, convergence speed as
well as model complexity, facilitating the choice of the best method for a given
application.},
	publisher = {Elsevier},
	journal = {Neurocomputing},
	volume = {275},
	pages = {1261-1274}
}