Regularizing axis-aligned ensembles via data rotations that favor simpler learners

DOI10.1007/s11222-020-09973-3zbMath1461.62013OpenAlexW3126012003MaRDI QIDQ2029102

Piotr Fryzlewicz, Rico Blaser

Publication date: 3 June 2021

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11222-020-09973-3

zbMATH Keywords

regularizationensemble learningrandom rotationminimal complexity

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08)

Related Items (1)

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Uses Software

• UCI-ml
• ElemStatLearn

Cites Work

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• Greedy function approximation: A gradient boosting machine.
• Constructing optimal binary decision trees is NP-complete
• Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions
• Generation of Random Orthogonal Matrices
• An Introduction to Statistical Learning
• Random-projection Ensemble Classification
• Fast Computation of Rotation-Invariant Image Features by an Approximate Radial Gradient Transform
• Random forests

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