Machine Learning Methods Embedded With Domain Knowledge (Part II): Generalization Risk

被引：0

作者：

Shang Y. ^{[1
]}

Guo J. ^{[2
]}

Wu W. ^{[1
]}

Su J. ^{[2
]}

Liu W. ^{[2
]}

Zhuang S. ^{[3
]}

Zhou L. ^{[2
]}

机构：

[1] State Key Lab of Control and Simulation of Power Systems and Generation Equipments, Tsinghua University, Haidian District, Beijing

[2] China Electric Power Research Institute, Haidian District, Beijing

[3] North China Electric Power University, Changping Distirct, Beijing

来源：

Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering | 2019年 / 39卷 / 16期

关键词：

Data driven; Generalization risk; Knowledge guiding; Machine learning; Statistical learning theory;

D O I：

10.13334/j.0258-8013.pcsee.190479

中图分类号：

学科分类号：

摘要：

The theoretical achievements of data-driven machine learning models (DDM) were briefly reviewed. Then, the generalization risks of knowledge-guiding & data-driven machine learning model (KDM) in both the local learning space and global learning space were analyzed. The results show that, under certain assumptions, KDM can bound its generalization error in the local learning space approaching probability 1, and bound its generalization error in the global learning space more tightly than the generalization error of DDM with some probability 1-δ. Compared with DDM, KDM is more efficient and robust under the circumstances with limited training samples. © 2019 Chin. Soc. for Elec. Eng.

引用

页码：4641 / 4649

页数：8

共 22 条

[1] Shang Y., Guo J., Wu W., Et al., Machine learning methods embedded with domain knowledge(part I): model analysis, Proceedings of the CSEE, 39, 15, pp. 4406-4415, (2019)
[2] Vapnik V.N., The Nature of Statistical Learning Theory, (1999)
[3] Goodfellow I., Bengio Y., Courville A., Deep Learning, (2016)
[4] Liang P., CS229T/STAT231: Statistical learning theory (Winter 2016)
[5] Zhang X., Introduction to statistical learning theory and support vector machines, Acta Automatica Sinica, 26, 1, pp. 32-42, (2000)
[6] Chen B.T.Q., Rubanova Y., Bettencourt J., Et al., Neural ordinary differential equations, Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 6572-6583, (2018)
[7] Lei N., Su K., Cui L., Et al., A geometric view of optimal transportation and generative model, (2017)
[8] Kong W., Valiant G., Estimating learnability in the sublinear data regime, Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 5460-5469, (2018)
[9] Carmon Y., Duchi J.C., Analysis of Krylov subspace solutions of regularized nonconvex quadratic problems, Proceedings of the 32nd International Conference on Neural Information Processing Systems, pp. 10728-10738, (2018)
[10] Sutton R.S., Barto A.G., Reinforcement Learning: an Introduction, (2018)

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