Abstract
Relevance Vector Machine (RVM) is an important learning method in the field of machine learning for its sparsity, global optimality and the ability to solve nonlinear problems by using kernel functions. Biased wavelets are localized in time and infrequency but, unlike wavelets, have adjustable nonzero mean. The proposed hybrid algorithm employs a family of biased wavelets to construct the kernel functions of RVM, which makes the kernel of RVM more flexible. RVM models are trained according to the diversity of the signal, and the predicted variance are selected in the hybrid algorithm to improve the accuracy. Test results show that RVM with the biased wavelet kernel is able to get increased prediction precision considering data features and the predicted variance is an efficient metric to construct the hybrid algorithm.
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Liu, F., Zhao, F., Tong, M., Yang, Y., Yu, Z. (2017). Hybrid RVM Algorithm Based on the Prediction Variance. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10634. Springer, Cham. https://doi.org/10.1007/978-3-319-70087-8_6
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DOI: https://doi.org/10.1007/978-3-319-70087-8_6
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