Abstract
A new Weighted Multiple Support Vector Regression Models Based on Clustering Algorithm is proposed to model dynamic system. Firstly, SOM and k-means clustering algorithm is applied to partition the whole training set into several disjointed regions. Secondly, the best weighted combination of different kernel function of SVR fit each partitioned cluster. Finally, this new approach is applied to time-series prediction problems, the results show that the learning strategy has effective improvement in the generalization performance in comparison with the single SVR model.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 61572073), Beijing University of Science and Technology Central University Fundamental Research Funds (FRF-BD-17-002A).
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Wang, L., Li, K., Ma, Q. (2020). Weighted Multiple Support Vector Regression Models Based on Clustering Algorithm. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_67
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DOI: https://doi.org/10.1007/978-981-32-9698-5_67
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