Funder
National Institutes of Health
Publisher
Springer Science and Business Media LLC
Subject
Computer Science Applications,Hardware and Architecture,Theoretical Computer Science,Software
Reference17 articles.
1. T. Chen, C. Guestrin, XGBoost: a scalable tree boosting system, in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16. ACM, New York, NY, USA, pp. 785–794 (2016). https://doi.org/10.1145/2939672.2939785
2. M. Fink, P. Perona, Mutual boosting for contextual inference, in Advances in Neural Information Processing Systems. ed. by S. Thrun, L.K. Saul, B. Schölkopf, vol. 16, pp. 1515–1522 (2004). https://proceedings.neurips.cc/paper/2003/file/070dbb6024b5ef93784428afc71f2146-Paper.pdf
3. Y. Freund, R.E. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
4. J.H. Friedman, Greedy function approximation: a gradient boosting machine. Ann. stat. 29, 1189–1232 (2001)
5. GPLearn. https://gplearn.readthedocs.io/ (2020). Accessed 20 Nov 2020
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