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《CHINESE JOURNAL OF COMPUTERS》 1999-08
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AN APPROACH FOR ESTIMATING PARAMETERS IN GAUSSIAN MIXTURE MODEL BASED ON MINIMUM CLASSIFICATION ERROR RATE

MA Ji Yong GAO Wen (Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001) (Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080)  
The traditional approach for estimating parameters in Gaussian Mixture Models(GMM) based on maximum likelihood is a kind of unsupervised learning method, its shortage is that the parameters in GMM are derived only by the training samples in one class without taking into account the effect of sample distributions of other classes, hence, its recognition is usually not ideal. This paper presents an approach for estimating parameters in GMM based on the minimum classification error rate of different classes, this method takes into account the discriminations of samples in different classes. To increase the possibility of obtaining the global optimal solution, this paper proposes an approach for estimating the optimal parameters in GMM based on Evolutionary Programming. An experiment has been conducted using the method for text independent speaker recognition, the results have shown that the recognition accuracy is higher than that of the traditional approach.
【Fund】: 国家八六三高技术研究发展计划 国家自然科学基金
【CateGory Index】: TP18
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【References】
Chinese Journal Full-text Database 1 Hits
1 DENG Aidong1 ZHAO Li2 BAO Yongqiang3 (1. National Engineering Research Center of Turbo-generator Vibration, Southeast University, Nanjing 210096; 2 . School of Information Science and Engineering, Southeast University, Nanjing 210096; 3. School of Information Engineering, Nanjing Institute of Technology, Nanjing 210067);Recognition of Rub-impact Acoustic Emission Signal Based on Fuzzy Entropy[J];Journal of Mechanical Engineering;2010-03
【Co-references】
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10 WANG Wei Qiang 1) GAO Wen 1),2) 1) (Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100080) 2) (Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin 150001);A Fast Caption Text Detection Algorithm on MPEG Compressed Video and Its Application[J];Chinese Journal of Computers;2001-06
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