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Localization of Concept Drift: Identifying the Drifting Datapoints
2022 International Joint Conference on Neural Networks (IJCNN), 2022The notion of concept drift refers to the phenomenon that the distribution which is underlying the observed data changes over time. As a consequence machine learning models may become inaccurate and need adjustment. While there do exist methods to detect concept drift, to find change points in data streams, or to adjust models in the presence of ...
Hinder, Fabian +4 more
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Dynamic Gate Stress Induced Threshold Voltage Drift of Silicon Carbide MOSFET
IEEE Electron Device Letters, 2020For silicon carbide (SiC) power MOSFETs, threshold voltage drift is a remaining obstacle in their way to the market. This study experimentally investigates the drift under dynamic or switching gate stresses.
Huaping Jiang +5 more
semanticscholar +1 more source
A drift of the drift adjustment method [PDF]
, 2002 This paper shows why regressing the realised rates of depreciation within the exchange rate band on a given information set and conditional on (ex-post) actual no-realignment (à la drift adjustment) still encounters a Peso Problem. Such a procedure generally gives inconsistent estimates. The main reason is that the frequency of realignments in the data
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European Journal of Information Systems, 2005
Received: 27 September 2005 Revised: 28 September 2005 Accepted: 29 September 2005 I first met Claudio in Oslo around 1980. Like so many others we met because of Kristen Nygaard. At this time Claudio spent some months at the University of Oslo as a visiting scholar doing research (together with Leslie Schneider) on the practical impacts of the ‘data ...
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Received: 27 September 2005 Revised: 28 September 2005 Accepted: 29 September 2005 I first met Claudio in Oslo around 1980. Like so many others we met because of Kristen Nygaard. At this time Claudio spent some months at the University of Oslo as a visiting scholar doing research (together with Leslie Schneider) on the practical impacts of the ‘data ...
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Drift mining in data: A framework for addressing drift in classification
Computational Statistics & Data Analysis, 2013zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hofer, Vera, Krempl, Georg
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Concept Drift Adaptation by Exploiting Drift Type
ACM Transactions on Knowledge Discovery from DataConcept drift is a phenomenon where the distribution of data streams changes over time. When this happens, model predictions become less accurate. Hence, models built in the past need to be re-learned for the current data. Two design questions need to be addressed in designing a strategy to re-learn models: which type of concept drift has occurred, and
Jinpeng Li +4 more
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Low-drift and real-time lidar odometry and mapping
Auton. Robots, 2017Ji Zhang, Sanjiv Singh
semanticscholar +1 more source
Brazilian Symposium on Artificial Intelligence, 2004
João Gama +3 more
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João Gama +3 more
semanticscholar +1 more source
A Survey on Concept Drift in Process Mining
ACM Computing Surveys, 2022Denise Maria Vecino Sato +1 more
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