scientific article; zbMATH DE number 7255135

MaRDI QIDQ4969173

Jaewon Chung, Jason Yim, Randal Burns, Tyler M. Tomita, Benjamin Falk, Cencheng Shen, Jesse L. Patsolic, James C. Browne, Carey E. Priebe, Mauro Maggioni, Joshua T. Vogelstein

Publication date: 5 October 2020

Full work available at URL: https://arxiv.org/abs/1506.03410

Title: zbMATH Open Web Interface contents unavailable due to conflicting licenses.

zbMATH Keywords

classificationfeature extractionregressiondecision treesrandom forestsensemble learningsparse learningrandom projections

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Related Items (2)

Manifold Oblique Random Forests: Towards Closing the Gap on Convolutional Deep Networks ⋮ Shape modeling with spline partitions

Uses Software

• Rcpp
• MNIST
• ranger
• xgboost
• XGBoost
• LightGBM
• rerf
• BOHB
• obliqueRF
• Tunability

Cites Work

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• Variance and bias for general loss functions
• Database-friendly random projections: Johnson-Lindenstrauss with binary coins.
• Arcing classifiers. (With discussion)
• Consistency of random forests
• Random Projection Trees for Vector Quantization
• Random forests

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