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- W1998035802 abstract "Gaussian mixture modeling is a powerful approach for data analysis and the determination of the number of Gaussians, or clusters, is actually the problem of Gaussian mixture model selection which has been investigated from several respects. This paper proposes a new kind of automated model selection algorithm for Gaussian mixture modeling via an entropy penalized maximum-likelihood estimation. It is demonstrated by the experiments that the proposed algorithm can make model selection automatically during the parameter estimation, with the mixing proportions of the extra Gaussians attenuating to zero. As compared with the BYY automated model selection algorithms, it converges more stably and accurately as the number of samples becomes large." @default.
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- W1998035802 date "2004-12-01" @default.
- W1998035802 modified "2023-10-02" @default.
- W1998035802 title "ENTROPY PENALIZED AUTOMATED MODEL SELECTION ON GAUSSIAN MIXTURE" @default.
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- W1998035802 doi "https://doi.org/10.1142/s0218001404003812" @default.
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