Concept Drift Learning with Alternating Learners

18 Oct 2017 Yunwen Xu Rui Xu Weizhong Yan Paul Ardis

Data-driven predictive analytics are in use today across a number of industrial applications, but further integration is hindered by the requirement of similarity among model training and test data distributions. This paper addresses the need of learning from possibly nonstationary data streams, or under concept drift, a commonly seen phenomenon in practical applications... (read more)

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