Active Learning: Applications, Foundations and Emerging Trends
Sprache des Titels:
Englisch
Original Kurzfassung:
Active learning optimizes the interaction between artificial data mining/machine learning systems and humans.
For example, its techniques are used for selecting the most relevant information to be requested, or for determining
the most informative experiment to be performed. With increasing volumes of data, contrasting limited human
supervision capacities, the optimization of this interaction has become even more important. Hence, active
sampling and data acquisition techniques could contribute to the design and modeling of highly intelligent
learning systems.
This tutorial presents the basic techniques for pool-based and on-line active learning for streams. It contains
a summary of the common concepts like version space partitioning, uncertainty sampling and decision theoretic
approaches, and shortly mentions the connection between reinforcement learning and active learning. We show
how these concepts can be used in data streams and on-line applications, and discuss the main challenges of
stream active learning. Finally, we evaluate frameworks for pool-based and stream-based active learning to
validate if a method is applicable for a specific demand