Methods › General › Generalized Additive Models › Base Boosting

Base Boosting

2 papers tagged archive 2025-07-28

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.

A special case is the coordinate functional: γ(X) = πⱼ(X) = Xⱼ where Xⱼ denotes a prediction generated by the base model.

PaperSourceSee Code · a-wozniakowski/scikit-physlearn

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.

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.

TaskPapers
Anomaly Detection1
Anomaly Detection In Surveillance Videos1
BIG-bench Machine Learning1
Few-Shot Learning1
Multi-target regression1
Optical Character Recognition1
Time Series1
Time Series Analysis1

Usage over time archive 2025-07-28

Papers per year tagged with Base Boosting: 2020 to 2021, peak 1 1 0 2020: 1 paper 2020 2021: 1 paper 2021
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Generalized Additive Models

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