Managing Domain Knowledge and Multiple Models with Boosting
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1144-1148.
Abstract
We present MBoost, a novel extension to AdaBoost that extends boosting to use multiple weak learners explicitly, and provides robustness to learning models that overfit or are poorly matched to data. We demonstrate MBoost on a variety of problems and compare it to cross validation for model selection.
Peng Zang, and Charles Lee Isbell Jr.. 2007. Managing Domain Knowledge and Multiple Models with Boosting. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07. IJCAI, 1144–1148. https://ijcai.org/proceedings/2007/185
Zang, P., & Jr., C. L. I. (2007). Managing Domain Knowledge and Multiple Models with Boosting. In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 (pp. 1144–1148). IJCAI. https://ijcai.org/proceedings/2007/185
@inproceedings{ijcai2007p185,
title = {Managing Domain Knowledge and Multiple Models with Boosting},
author = {Zang, Peng and Jr., Charles Lee Isbell},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1144--1148},
year = {2007},
url = {https://ijcai.org/proceedings/2007/185},
}
TY - CPAPER TI - Managing Domain Knowledge and Multiple Models with Boosting AU - Zang, Peng AU - Jr., Charles Lee Isbell T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1144 EP - 1148 UR - https://ijcai.org/proceedings/2007/185 ER -Copied
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