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771-A Survey on Concept Drift Adaptation.pdf
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上传时间: 2020-03-29
详细说明：Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning
in this paper we characterize adaptive learning process, categorize existing strategies for handling concept
drift, discuss the most representative, distinct and popular techniques and algorithms, discuss evaluation
methodology of adaptive algorithms, and present a set of illustrative applications. This introduction to the
concept drift adaptation presents the state of the art techniques and a collection of benchmarks for researchers, industry analysts and practitioners. The survey aims at covering the different facets of concept
drift in an integrated way to reflect on the existing scattered state-of-the-art
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