Improving boosting methods with a stable loss function handling outliers-Reference-Cited by-同舟云学术

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Improving boosting methods with a stable loss function handling outliers

Published:2023-01-19 Issue:7 Volume:14 Page:2333-2352
ISSN:1868-8071
Container-title:International Journal of Machine Learning and Cybernetics
language:en
Short-container-title:Int. J. Mach. Learn. & Cyber.

Author:

Chao Wang^ORCID,Bo Li,Lei Wang,Pai Peng

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Software

Link

https://link.springer.com/content/pdf/10.1007/s13042-022-01766-6.pdf

Reference36 articles.

1. Hastie T, Tibshirani R, Buja A (1994) Flexible discriminant analysis by optimal scoring. J Am Stat Assoc 89(428):1255–1270

2. Friedman J, Hastie T, Tibshirani R (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337–407

3. Pu Z, Rao R (2018) Exponential stability criterion of high-order bam neural networks with delays and impulse via fixed point approach. Neurocomputing 292:63–71

4. Gao Y, Wen J, Peng L (2019) New exponential stability criterion for switched linear systems with average dwell time. Proc Inst Mech Eng Part I J Syst Control Eng 233(8):935–944

5. Cao J, Kwong S, Wang R (2012) A noise-detection based adaboost algorithm for mislabeled data. Pattern Recogn 45(12):4451–4465

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