A Machine Learning Approach to the Vector Prediction of Moments of Finite Normal Mixtures-Reference-Cited by-同舟云学术

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A Machine Learning Approach to the Vector Prediction of Moments of Finite Normal Mixtures

Published:2020 Issue: Volume: Page:307-314
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Gorshenin Andrey,Kuzmin Victor

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-39216-1_27

Reference17 articles.

1. Cai, T.T., Ma, J., Zhang, L.: CHIME: clustering of high-dimensional Gaussian mixtures with EM algorithm and its optimality. Ann. Stat. 47(3), 1234–1267 (2019). https://doi.org/10.1214/18-AOS1711

2. Ben Hassen, H., Masmoudi, K., Masmoudi, A.: Model selection in biological networks using a graphical EM algorithm. Neurocomputing. 349, 271–280 (2019). https://doi.org/10.1016/j.neucom.2019.03.047

3. Zeller, C.B., Cabral, C.R.B., Lachos, V.H., Benites, L.: Finite mixture of regression models for censored data based on scale mixtures of normal distributions. Adv. Data Anal. Classif. 13(1), 89–116 (2019). https://doi.org/10.1007/s11634-018-0337-y

4. Lee, S.X., Leemaqz, K.L., McLachlan, G.J.: A block EM algorithm for multivariate skew normal and skew t-mixture models. IEEE Trans. Neural Netw. Learn. Syst. 29(11), 5581–5591 (2018). https://doi.org/10.1109/TNNLS.2018.2805317

5. Wu, D., Ma, J.: An effective EM algorithm for mixtures of Gaussian processes via the MCMC sampling and approximation. Neurocomputing 331, 366–374 (2019). https://doi.org/10.1016/j.neucom.2018.11.046

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1. On Feature Expansion with Finite Normal Mixture Models in Machine Learning;Advances in Intelligent Systems, Computer Science and Digital Economics II;2021

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