Learning monotonic constraints for scientific regression: A statistical gradient boosting framework

Asela Hevapathige

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DOI: 10.1007/s44443-026-00742-2
Published: 2026-05-05
Container: Journal of King Saud University Computer and Information Sciences
Publisher: Springer Science and Business Media LLC
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1007/s44443-026-00742-2,
  title = {Learning monotonic constraints for scientific regression: A statistical gradient boosting framework},
  author = {Asela Hevapathige},
  year = {2026},
  journal = {Journal of King Saud University Computer and Information Sciences},
  doi = {10.1007/s44443-026-00742-2},
  url = {https://doi.org/10.1007/s44443-026-00742-2}
}

RIS

TY  - JOUR
TI  - Learning monotonic constraints for scientific regression: A statistical gradient boosting framework
AU  - Asela Hevapathige
PY  - 2026
JO  - Journal of King Saud University Computer and Information Sciences
DO  - 10.1007/s44443-026-00742-2
UR  - https://doi.org/10.1007/s44443-026-00742-2
ER  -

APA

Hevapathige, A. (2026). Learning monotonic constraints for scientific regression: A statistical gradient boosting framework. Journal of King Saud University Computer and Information Sciences. https://doi.org/10.1007/s44443-026-00742-2

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crossref · retrieved 2026-11-10T10:45:02.211Z