{"url":"/method/base-boosting","slug":"base-boosting","name":"Base Boosting","full_name":"Base Boosting","full_name_withheld":false,"description_markdown":"Base boosting is a generalization of gradient boosting, which fits a hybrid additive and varying coefficient model. \r\n\r\n- Namely, gradient boosting fits an additive model:\r\n\\begin{equation}\r\nh(X ; \\{ \\alpha, \\theta\\}) = \\alpha_{0} + \\sum_{k=1}^{K} \\alpha_{k} b(X ; \\theta_{k}),\r\n\\end{equation}\r\nwhere the boosting mechanism begins optimization in function space at a constant model.\r\n\r\n- In contrast, base boosting fits the hybrid additive and varying coefficient model:\r\n\\begin{equation}\r\nh(X ; \\{ \\alpha, \\theta\\}) = \\gamma(X) + \\sum_{k=1}^{K} \\alpha_{k} b(X ; \\theta_{k}),\r\n\\end{equation}\r\nwhere the boosting mechanism begins optimization in function space at a base model, which may be a non-constant model.\r\n\r\nA special case is the coordinate functional:\r\n\\begin{equation}\r\n\\gamma(X) = \\pi_{j}(X) = X_{j}\r\n\\end{equation}\r\nwhere $X_{j}$ denotes a prediction generated by the base model.\r\n\r\n- This setup facilitates knowledge transfer between the base model and boosting mechanism.","description_state":"present","introduced_year":null,"introduced_by":{"title":"Boosting on the shoulders of giants in quantum device calibration","paper":"/paper/boosting-on-the-shoulders-of-giants-in","first_author":"Alex Wozniakowski","n_authors":4,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/boosting-on-the-shoulders-of-giants-in"},"source":{"url":"https://arxiv.org/abs/2005.06194v1","title":"Boosting on the shoulders of giants in quantum device calibration","url_on_a_paper_host":true},"code_snippet_url":"https://github.com/a-wozniakowski/scikit-physlearn/blob/03f944c4fecf512ee763ec4552f30c4a3565612d/physlearn/supervised/regression.py#L581","code_snippet_url_on_a_code_host":true,"categories":[{"area":"General","area_id":"general","collection":"Generalized Additive Models","url":"/methods/category/generalized-additive-models","pwc_aliases":[]}],"n_papers_tagged":2,"archive_num_papers":2,"papers_newest_first":[{"paper":"/paper/real-time-anomaly-detection-and-feature","title":"Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance Video","date":"2021-05-11","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":"/paper/boosting-on-the-shoulders-of-giants-in","title":"Boosting on the shoulders of giants in quantum device calibration","date":"2020-05-13","arxiv_id":"2005.06194","n_code_links":1,"syntology":null}],"papers_shown":2,"tasks":[{"task":"/task/anomaly-detection","name":"Anomaly Detection","papers":1},{"task":"/task/anomaly-detection-in-surveillance-videos","name":"Anomaly Detection In Surveillance Videos","papers":1},{"task":"/task/machine-learning","name":"BIG-bench Machine Learning","papers":1},{"task":"/task/few-shot-learning","name":"Few-Shot Learning","papers":1},{"task":"/task/multi-target-regression","name":"Multi-target regression","papers":1},{"task":"/task/optical-character-recognition","name":"Optical Character Recognition","papers":1},{"task":"/task/time-series-1","name":"Time Series","papers":1},{"task":"/task/time-series","name":"Time Series Analysis","papers":1}],"tasks_shown":8,"n_tasks":8,"usage_by_year":[{"year":"2020","papers":1},{"year":"2021","papers":1}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/base-boosting"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}