XG Boosting and Deep Random Forest Based House Number Detection
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-9819-5_19
Reference15 articles.
1. Sun, L.: Application and improvement of Xgboost algorithm based on multiple parameter optimization strategy. In: 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), pp. 1822–1825. IEEE, Dec 2020
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3. Jha, S., Roy, A.: On detection of out of distribution inputs in deep neural networks. In: 2021 IEEE Third International Conference on Cognitive Machine Intelligence (CogMI), pp. 282–288. IEEE, Dec 2021
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5. Le Nguyen, K.: Application of XGBoost model for predicting the dynamic response of high-speed railway bridges. In: CIGOS 2021, Emerging Technologies and Applications for Green Infrastructure, pp. 1765–1773. Springer, Singapore (2022)
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