Abstract—Regional science and technology innovation policies have important strategic significance for China to build an innovative country. Scientific and objective evaluation of the effects of science and technology innovation policies is the main problem to be solved at present. In this context, this paper takes Beijing as an example to evaluate the effects of scientific and technological innovation policies by using lasso and BP neural network algorithms. The empirical results show that supply-driven policies have obvious effects, and the effects show an upward trend year by year, while the environmental policies have a significant decline, and the demand-driven policies still have much room for improvement. Finally, the next research direction is proposed.
Index Terms—Scientific and technological innovation, policy effects, BP neural network, Lasso.
Y. Zhang and Y. Guan are with the College of Economic and Management, Beijing University of Technology, 100022 China (Corresponding Author: Y. Guan; e-mail: bjutzhya@bjut.edu.cn; 875749156@qq.com).
Cite: Zhang Yongan and Guan Yongjuan, "Evaluation of Regional Science and Technology Innovation Policy Effect Based on Lasso and BP Neural Network," International Journal of Machine Learning and Computing vol. 10, no. 1, pp. 69-74, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).