@Article{2019.100000068,
AUTHOR = {He Ni, Yongqiao Wang, Buyun Xu},
TITLE = {Self-Organizing Gaussian Mixture Map Based on Adaptive Recursive  Bayesian Estimation},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {2},
PAGES = {227--236},
URL = {http://www.techscience.com/iasc/v26n2/39940},
ISSN = {2326-005X},
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.},
DOI = {10.31209/2019.100000068}
}