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Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation

He Ni1,*, Yongqiao Wang1, Buyun Xu2

1 School of Finance, Zhejiang Gongshang University, Hangzhou, China
2 Hangzhou College of Commerce, Zhejiang Gongshang University, Hangzhou, Tonglu, China

* Corresponding Author: He Ni, email

Intelligent Automation & Soft Computing 2020, 26(2), 227-236. https://doi.org/10.31209/2019.100000068

Abstract

The paper presents a probabilistic clustering approach based on self-organizing learning algorithm and recursive Bayesian estimation. The model is built upon the principle that the market data space is multimodal and can be described by a mixture of Gaussian distributions. The model parameters are approximated by a stochastic recursive Bayesian learning: searches for the maximum a posterior solution at each step, stochastically updates model parameters using a “dualneighbourhood” function with adaptive simulated annealing, and applies profile likelihood confidence interval to avoid prolonged learning. The proposed model is based on a number of pioneer works, such as Mixture Gaussian Autoregressive Model, Self-Organizing Mixture Map, and have some favoured attributes on its robust convergence and good generalization. The experimental results on both artificial and real market data show that the algorithm is a good alternative in measuring multimodal distribution.

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Cite This Article

APA Style
Ni, H., Wang, Y., Xu, B. (2020). Self-organizing gaussian mixture map based on adaptive recursive bayesian estimation. Intelligent Automation & Soft Computing, 26(2), 227-236. https://doi.org/10.31209/2019.100000068
Vancouver Style
Ni H, Wang Y, Xu B. Self-organizing gaussian mixture map based on adaptive recursive bayesian estimation. Intell Automat Soft Comput . 2020;26(2):227-236 https://doi.org/10.31209/2019.100000068
IEEE Style
H. Ni, Y. Wang, and B. Xu "Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive Bayesian Estimation," Intell. Automat. Soft Comput. , vol. 26, no. 2, pp. 227-236. 2020. https://doi.org/10.31209/2019.100000068



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