Foundations of Interactive Adaptive Learning

                                 Georg Krempl

       Information and Computing Sciences, Utrecht University, Utrecht
                           g.m.krempl@uu.nl



    Abstract. This part starts with the classic stream mining paradigm.
    In its context, we discuss the challenges posed by non-stationarity and
    limitations in processing, storage, and supervision capacities. We briefly
    summarize related techniques, e.g. for incremental processing, forgetting,
    and change detection. Furthermore, we introduce techniques for optimis-
    ing the interaction of a machine learning system with an oracle such as a
    human supervisor. We review active machine learning techniques, with
    focus on adaptive active learning for evolving and streaming data. We
    discuss recent advances and conclude with an overview on open research
    questions in adaptive active machine learning.




c 2019 for this paper by its authors. Use permitted under CC BY 4.0.


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