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• W3084853684 endingPage "3605" @default.
• W3084853684 startingPage "3592" @default.
• W3084853684 abstract "Quadratic programming is the process of solving a special type of mathematical optimization problem. Recent advances in online solutions for quadratic programming problems (QPPs) have created opportunities to widen the scope of applications for support vector regression (SVR). In this vein, efforts to make SVR compatible with streaming data have been met with substantial success. However, streaming data with concept drift remain problematic because the trained prediction function in SVR tends to drift as the data distribution drifts. Aiming to contribute a solution to this aspect of SVR's advancement, we have developed continuous SVR (C-SVR) to solve regression problems with nonstationary streaming data, that is, data where the optimal input-output prediction function can drift over time. The basic idea of C-SVR is to continuously learn a series of input-output functions over a series of time windows to make predictions about different periods. However, strikingly, the learning process in different time windows is not independent. An additional similarity term in the QPP, which is solved incrementally, threads the various input-output functions together by conveying some learned knowledge through consecutive time windows. How much learned knowledge is transferred is determined by the extent of the concept drift. Experimental evaluations with both synthetic and real-world datasets indicate that C-SVR has better performance than most existing methods for nonstationary streaming data regression." @default.
• W3084853684 created "2020-09-21" @default.
• W3084853684 creator A5034696006 @default.
• W3084853684 creator A5062192039 @default.
• W3084853684 creator A5062917240 @default.
• W3084853684 date "2022-05-01" @default.
• W3084853684 modified "2023-09-27" @default.
• W3084853684 title "Continuous Support Vector Regression for Nonstationary Streaming Data" @default.
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• W3084853684 doi "https://doi.org/10.1109/tcyb.2020.3015266" @default.
• W3084853684 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32915757" @default.
• W3084853684 hasPublicationYear "2022" @default.
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