Methods › General › Generalized Additive Models › Base Boosting
Base Boosting
Introduced by Alex Wozniakowski et al. in Boosting on the shoulders of giants in quantum device calibration
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
Base boosting is a generalization of gradient boosting, which fits a hybrid additive and varying coefficient model.
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Namely, gradient boosting fits an additive model: h(X ; { α, θ}) = α₀ + ∑ₖ₌₁ᴷ αₖ b(X ; θₖ), where the boosting mechanism begins optimization in function space at a constant model.
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In contrast, base boosting fits the hybrid additive and varying coefficient model: h(X ; { α, θ}) = γ(X) + ∑ₖ₌₁ᴷ αₖ b(X ; θₖ), where the boosting mechanism begins optimization in function space at a base model, which may be a non-constant model.
A special case is the coordinate functional: γ(X) = πⱼ(X) = Xⱼ where Xⱼ denotes a prediction generated by the base model.
- This setup facilitates knowledge transfer between the base model and boosting mechanism.
Papers archive 2025-07-28
2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video 11 May 2021 · 1 repository
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Boosting on the shoulders of giants in quantum device calibration 13 May 2020 · 1 repository · arXiv:2005.06194
Tasks archive 2025-07-28
8 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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