Abstract
This paper establishes random multi-scale Gaussian kernels based relevance vector machine (RMGK-RVM) for regression learning problems. The mixture of multiple Gaussian kernels with different scale parameters sampled from some predefined distribution is used as the model. Under the Bayesian inference framework, RMGK-RVM can learn the whole distribution of the prediction variable. In this way, the uncertainties of input data are fully considered and the prediction accuracy of the target variable is improved for complicated data. The experimental results on one simulation data and three real-life data sets show that the proposed method performs favorably.
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Acknowledgments
This work is supported partly by First Class Discipline of Zhejiang-A (Zhejiang Gongshang University - Statistics), National Natural Science Foundation of China under grant 11571031.
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Gu, Y., Dong, X., Shi, J., Kong, X. (2020). Random Multi-scale Gaussian Kernels Based Relevance Vector Machine. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 592. Springer, Singapore. https://doi.org/10.1007/978-981-32-9682-4_33
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DOI: https://doi.org/10.1007/978-981-32-9682-4_33
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