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• W3111330250 abstract "Piecewise linear L1-regression problem is formulated as an unconstrained difference of convex (DC) optimization problem and an algorithm for solving this problem is developed. Auxiliary problems are introduced to design an adaptive approach to generate a suitable piecewise linear regression model and starting points for solving the underlying DC optimization problems. The performance of the proposed algorithm as both approximation and prediction tool is evaluated using synthetic and real-world data sets containing outliers. It is also compared with mainstream machine learning regression algorithms using various performance measures. Results demonstrate that the new algorithm is robust to outliers and in general, provides better predictions than the other alternative regression algorithms for most data sets used in the numerical experiments." @default.
• W3111330250 created "2020-12-21" @default.
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• W3111330250 date "2020-12-08" @default.
• W3111330250 modified "2023-10-14" @default.
• W3111330250 title "Robust piecewise linear <i>L</i>₁-regression via nonsmooth DC optimization" @default.
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• W3111330250 doi "https://doi.org/10.1080/10556788.2020.1855171" @default.
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