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《Acta Electronica Sinica》 2011-S1
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Unsupervised Learning for Finite Mixture Models Based on BIC Criterion and Gibbs Sampling

LIU Wei-feng,YANG Ai-lan(School of Automation,Hangzhou Dianzi University,Hangzhou,Zhejiang 310018 China)  
An unsupervised learning algorithm for finite mixture models(FMM) by using the BIC criterion and the Gibbs sampling is proposed.The FMM parameters are estimated by the Gibbs sampling algorithm.The number of the models is further given by calculating the BIC criterion.In the final simulations,we propose two examples of Gaussian mixture models,where the FMM with different number of components is adopted to fit observation data.The final result shows that the proposed algorithm can effectively estimate the parameters and the number of the components.
【Fund】: 国家自然科学基金(No.91016020 No.60934009);; 浙江省自然科学基金(No.Y1101218);; 杭州电子科技大学科研启动基金(No.KYS065609051)
【CateGory Index】: TP18
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