Abstract
Support vector regression (SVR) is a widely used regression technique for its competent performance. However, non-linear SVR is time consuming for large-scale tasks due to the dimension curse of kernelization. Recently, a budgeted stochastic gradient descent (BSGD) method has been developed to train large-scale kernelized SVC. In this paper, we extend the BSGD method to non-linear regression tasks. According to the performance of different budget maintenance strategies, we combine the stochastic gradient descent (SGD) method with the merging strategy. Experimental results on real-world datasets show that the proposed kernelized SVR with BSGD can achieve competent accuracy, with good computational efficiency compared to some state-of-the-art algorithms.
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Acknowledgements
This work is supported by the National Natural Science Foundation of China under Grant Nos. 61432011, U1435212, and 61105054. This article is also supported by Commission for Collaborating Research Program, Key Laboratory of Solar Activity, National Astronomical Observatories, Chinese Academy of Sciences with KLSA201403.
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Xie, Z., Li, Y. Large-scale support vector regression with budgeted stochastic gradient descent. Int. J. Mach. Learn. & Cyber. 10, 1529–1541 (2019). https://doi.org/10.1007/s13042-018-0832-7
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DOI: https://doi.org/10.1007/s13042-018-0832-7