References
- Bai, J. and S. Ng. 2009. "Boosting Diffusion Indices." Journal of Applied Econometrics, vol. 24, no. 4, pp. 607-629. https://doi.org/10.1002/jae.1063
- Boser, B. E., Guyon, I. M. and V. N. Vapnik. 1992. "A Traning Algorithm for Optimal Margin Classifiers." Paper presented at COLT '92: Proceedings of 15th Annual Workshop on Computational Learning Theory, July 1992. pp. 144-152.
- Chen, T. and C. Guestrin. 2016. "XGBoost: A Scalable Tree Boosting System." Paper presented at KDD '16: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco California, USA, August 13- 17, 2016. arXiv:1603.02754v3. https://doi.org/10.1145/2939672.2939785.
- Freund, Y. and R. E. Schapire. 1997. "A Decision-theoretic Generalization of On-line Learning and an Application to Boosting." Journal of Computer and System Sciences, vol. 55, no. 1, pp. 119-139. https://doi.org/10.1006/jcss.1997.1504
- Guyon, I., Gunn, S., Nikravesh, M. and L. A. Zadeh. eds. 2006. Feature Extraction: Foundations and Applications. Series Studies in Fuzziness and Soft Computing, vol. 207. Berlin: Springer.
- Hsu, K.-W. 2017. "Heterogeneous AdaBoost with Stochastic Algorithm Selection." Paper presented at IMCOM '17: Proceedings of the 11th Internaitonal Confernce on Ubiquitous Information Management and Communication, Jan. 2017, Beppu. https://doi.org/10.1145/3022227.3022266. pp. 1-8.
- Huang, G., Liu, Z., Van Der Maaten, L. and K. Q. Weinberger. 2017. "Densely Connected Convolutional Networks." In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition.
- Li, X., Wang, L. and E. Sung. 2008. "AdaBoost with SVM-based Component Classifiers." Engineering Applications of Artificial Intelligence, vol. 21, no. 5, pp. 785-795. https://doi.org/10.1016/j.engappai.2007.07.001
- McCracken, M. W. and S. Ng. 2016. "FRED-MD: A Monthly Database for Macroeconomic Research." Journal of Business & Economic Statistics, vol. 34, no. 4, pp. 574-589. https://doi.org/10.1080/07350015.2015.1086655
- Ng, S. 2014. "Viewpoint: Boosting Recessions." Canadian Journal of Economics, vol. 47, no. 1, pp. 1-34. https://doi.org/10.1111/caje.12070
- Zhong, X. and D. Enke. 2019. "Predicting the Daily Return Direction of the Stock Market Using Hybrid Machine Learning Algorithms." Financial Innovation, vol. 5.