End-to-end learning of decision trees and forests

DOI10.1007/s11263-019-01237-6zbMath1477.68279OpenAlexW2979775090WikidataQ127102594 ScholiaQ127102594MaRDI QIDQ2193582

Julian F. P. Kooij, Thomas M. Hehn, Fred A. Hamprecht

Publication date: 20 August 2020

Published in: International Journal of Computer Vision (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11263-019-01237-6

zbMATH Keywords

interpretabilityefficient inferenceend-to-end learningdecision forests

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07)

Related Items (2)

One-stage tree: end-to-end tree builder and pruner ⋮ Unnamed Item

Uses Software

• UCI-ml
• C4.5
• PyTorch
• Adam
• SkipNet
• Fashion-MNIST
• MS-COCO
• Cityscapes
• obliqueRF

Cites Work

• Constructing optimal binary decision trees is NP-complete
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Random forests
• Unnamed Item
• Unnamed Item

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