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[1] Tianqi Chen,et al. XGBoost: A Scalable Tree Boosting System , 2016, KDD.
[2] George D. Magoulas,et al. Deep Incremental Boosting , 2016, GCAI.
[3] Zhi-Hua Zhou,et al. On the doubt about margin explanation of boosting , 2010, Artif. Intell..
[4] R. Schapire. The Strength of Weak Learnability , 1990, Machine Learning.
[5] Andrew Zisserman,et al. Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.
[6] Yoav Freund,et al. Boosting a weak learning algorithm by majority , 1995, COLT '90.
[7] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[8] Jonathan Brandt,et al. Robust object detection via soft cascade , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[9] Yoshua Bengio,et al. Boosting Neural Networks , 2000, Neural Computation.
[10] Alberto Maria Segre,et al. Programs for Machine Learning , 1994 .
[11] Paul A. Viola,et al. Robust Real-Time Face Detection , 2001, International Journal of Computer Vision.
[12] Yuanzhi Li,et al. A Convergence Theory for Deep Learning via Over-Parameterization , 2018, ICML.
[13] Zhiyuan He,et al. Gradient Boosting Machine: A Survey , 2019, ArXiv.
[14] Andrew Y. Ng,et al. Reading Digits in Natural Images with Unsupervised Feature Learning , 2011 .
[15] Trevor Hastie,et al. Multi-class AdaBoost ∗ , 2009 .
[16] Robert E. Schapire,et al. How boosting the margin can also boost classifier complexity , 2006, ICML.
[17] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.
[18] George D. Magoulas,et al. Boosted Residual Networks , 2017, EANN.
[19] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[20] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[21] Jian Yang,et al. Boosted Convolutional Neural Networks , 2016, BMVC.
[22] Paul A. Viola,et al. Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.
[23] J. Friedman. Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .
[24] J. Ross Quinlan,et al. Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.
[25] Harris Drucker,et al. Improving Performance in Neural Networks Using a Boosting Algorithm , 1992, NIPS.
[26] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[27] Yoav Freund,et al. Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.
[28] Mikhail Belkin,et al. Reconciling modern machine-learning practice and the classical bias–variance trade-off , 2018, Proceedings of the National Academy of Sciences.
[29] Peter L. Bartlett,et al. Boosting Algorithms as Gradient Descent , 1999, NIPS.
[30] Yoram Singer,et al. Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.
[31] Yoav Freund,et al. Experiments with a New Boosting Algorithm , 1996, ICML.
[32] Lawrence O. Hall,et al. A Comparison of Decision Tree Ensemble Creation Techniques , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Samy Bengio,et al. Understanding deep learning requires rethinking generalization , 2016, ICLR.
[34] Anna Veronika Dorogush,et al. CatBoost: gradient boosting with categorical features support , 2018, ArXiv.
[35] Stefan Babinec,et al. Incremental Learning of Convolutional Neural Networks , 2009, IJCCI.
[36] Michael Cogswell,et al. Why M Heads are Better than One: Training a Diverse Ensemble of Deep Networks , 2015, ArXiv.
[37] Nuno Vasconcelos,et al. Multiclass Boosting: Theory and Algorithms , 2011, NIPS.
[38] Yoshua Bengio,et al. AdaBoosting Neural Networks: Application to on-line Character Recognition , 1997, ICANN.
[39] Lorenzo Rosasco,et al. Theory of Deep Learning III: explaining the non-overfitting puzzle , 2017, ArXiv.
[40] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .