The Delaunay triangulation learner and its ensembles

DOI10.1016/j.csda.2020.107030OpenAlexW3035133905MaRDI QIDQ830534

Guosheng Yin, Yehong Liu

Publication date: 7 May 2021

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2020.107030

zbMATH Keywords

treemachine learningbaggingboostingensemble learningDelaunay trianglerandom crystal

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (2)

Stable parameterization of continuous and piecewise-linear functions ⋮ Variable resolution Poisson-disk sampling for meshing discrete fracture networks

Uses Software

• DeWall
• UCI-ml
• SciPy
• Scikit
• FSInteract
• XGBoost
• Voronoi
• Adam

Cites Work

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• Predictive learning via rule ensembles
• Greedy function approximation: A gradient boosting machine.
• Bagging predictors
• Node harvest
• Lectures on the nearest neighbor method
• Empirical characterization of random forest variable importance measures
• Multivariate adaptive regression splines
• Optimality of the Delaunay triangulation in \(\mathbb{R}^ d\)
• DeWall: a fast divide and conquer Delaunay triangulation algorithm in \(E^d\).
• Strong laws for Euclidean graphs with general edge weights
• The Error in Linear Interpolation at the Vertices of a Simplex
• Reinforcement Learning Trees
• Extremely randomized trees
• Random forests

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