Abstract
The mixture of Gaussian processes (MGP) is a powerful framework for machine learning. However, its parameter learning or estimation is still a very challenging problem. In this paper, a precise hard-cut EM algorithm is proposed for learning the parameters of the MGP without any approximation in the derivation. It is demonstrated by the experimental results that our proposed hard-cut EM algorithm for MGP is feasible and even outperforms two available hard-cut EM algorithms.
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Chen, Z., Ma, J., Zhou, Y. (2014). A Precise Hard-Cut EM Algorithm for Mixtures of Gaussian Processes. In: Huang, DS., Jo, KH., Wang, L. (eds) Intelligent Computing Methodologies. ICIC 2014. Lecture Notes in Computer Science(), vol 8589. Springer, Cham. https://doi.org/10.1007/978-3-319-09339-0_7
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DOI: https://doi.org/10.1007/978-3-319-09339-0_7
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09338-3
Online ISBN: 978-3-319-09339-0
eBook Packages: Computer ScienceComputer Science (R0)