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Using MaxSAT for efficient explanations of tree ensembles

• Authors : Ignatiev, Alexey; Izza, Yacine; Stuckey, Peter J. ...

Subjects: Constraint Satisfaction And Optimization (CSO); Machine Learning (ML); Philosophy And Ethics Of AI (PEAI)

• Source: Ignatiev , A , Izza , Y , Stuckey , P J & Marques-Silva , J 2022 , Using MaxSAT for efficient explanations of tree ensembles . in V

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Predicting Slaughter Weight in Pigs with Regression Tree Ensembles

• Authors : Alsahaf, A.; Azzopardi, G.; Ducro, B. ...

Subjects: Animal production; Ensemble learning; Gradient boosting

• Source: Alsahaf , A , Azzopardi , G , Ducro , B , Veerkamp , R F & Petkov , N 2018 , Predicting Slaughter Weight in Pigs with Regression Tree

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Tree canopy extent and height change in Europe, 2001–2021, quantified using Landsat data archive

• Authors : Turubanova, Svetlana; Potapov, Peter; Hansen, Matthew C.

Subjects: Europe; Forest; Forest change

• Source: Turubanova , S , Potapov , P , Hansen , M C , Li , X , Tyukavina , A , Pickens , A H , Guerra-Hernandez , J , Arranz , A P , Guerra-Hernandez , J , Senf , C , Häme , T , Valbuena , R , Eklundh , L ,

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Supervised learning approaches to modeling pedestal density

• Authors : Kit, Adam; Järvinen, Aaro; Frassinetti, L.

Subjects: fusion; machine learning; pedestal

• Source: Kit , A , Järvinen , A , Frassinetti , L , Wiesen , S & JET Contributors 2023 , ' Supervised learning approaches to modeling pedestal density ' , Plasma Physics and Controlled Fusion , vol. 65 , no.

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Tree canopy extent and height change in Europe, 2001–2021, quantified using Landsat data archive

• Authors : Turubanova, Svetlana; Potapov, Peter; Hansen, Matthew C.

Subjects: Europe; Forest; Forest change

• Source: Turubanova , S , Potapov , P , Hansen , M C , Li , X , Tyukavina , A , Pickens , A H , Guerra-Hernandez , J , Arranz , A P , Guerra-Hernandez , J , Senf , C , Häme , T , Valbuena , R , Eklundh , L ,

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Supervised learning approaches to modeling pedestal density

• Authors : Kit, Adam; Järvinen, Aaro; Frassinetti, L.

Subjects: fusion; machine learning; pedestal

• Source: Kit , A , Järvinen , A , Frassinetti , L , Wiesen , S & JET Contributors 2023 , ' Supervised learning approaches to modeling pedestal density ' , Plasma Physics and Controlled Fusion , vol. 65 , no.

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Euclid preparation:XXII. Selection of quiescent galaxies from mock photometry using machine learning

Subjects: Galaxies: evolution; Galaxies: general; Galaxies: high-redshift

• Source: Euclid Collaboration 2023 , ' Euclid preparation : XXII. Selection of quiescent galaxies from mock photometry using machine learning ' , Astronomy and Astrophysics , vol. 671 , A99 .

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Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly

• Authors : Lu, Yiwen; Yalcin, Dilek; Pigram, Paul J.

• Source: Lu , Y , Yalcin , D , Pigram , P J , Blackman , L D & Boley , M 2023 , ' Interpretable Machine Learning Models for Phase Prediction in Polymerization-Induced Self-Assembly ' , Journal of Chemical

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Left Ventricular Pressure Estimation Using Machine Learning-Based Heart Sound Classification

• Authors : Westphal, P.; Luo, H.X.; Shahmohammadi, M.

Subjects: heart sound; hemodynamics; cardiac resynchronization therapy

• Source: Westphal , P , Luo , H X , Shahmohammadi , M , Heckman , L I B , Kuiper , M , Prinzen , F W , Delhaas , T & Cornelussen , R N 2022 , ' Left Ventricular Pressure Estimation Using Machine Learning-Based

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Interpreting tree ensemble machine learning models with endoR

• Authors : Ruaud, Albane; Pfister, Niklas; Ley, Ruth E.

• Source: Ruaud , A , Pfister , N , Ley , R E & Youngblut , N D 2022 , ' Interpreting tree ensemble machine learning models with endoR ' , PLOS Computational Biology , vol. 18 ,

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