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SHIFFRIN R M, CHANDRAMOULI S H, GRNWALD P D. Bayes factors, relations to minimum description length, and overlapping model classes[J]. Journal of Mathematical Psychology, 2016, 72: 56-77.
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COVES T F, HRUSCHKA E R. Splitting and merging Gaussian mixture model components: An evolutionary approach[C]// 2011 10th International Conference on Machine Learning and Applications and Workshops. Piscataway, NY, USA: IEEE Press, 2011, 1: 106-111.
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SCRUCCA L. Identifying connected components in Gaussian finite mixture models for clustering[J]. Computational Statistics & Data Analysis, 2016, 93:5-17.
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LI Y, LI L. A greedy merge learning algorithm for Gaussian mixture model[C]// International Symposium on Intelligent Information Technology Application. Piscataway, NY, USA: IEEE Press, 2009:506-509.
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LI Y, LI L. A novel split and merge EM algorithm for Gaussian mixture model[C]// 2009 Fifth International Conference on Natural Computation. Piscataway, NY, USA: IEEE Press, 2009, 6: 479-483.
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COVES T F, HRUSCHKA E R, GHOSH J. Evolving Gaussian mixture models with splitting and merging mutation operators.[J]. Evolutionary Computation, 2016, 24(2):293.
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ZHU R, WANG L, ZHAI C, et al. High-dimensional variance-reduced stochastic gradient expectation-maximization algorithm[J]. Proceedings of Machine Learning Research, 2017, 70: 4180-4188.
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ASUNCION A, NEWMAN D H. UCI machine learning repository[EB/OL]. [2017-08-20]. http://archive.ics.uci.edu/ml/index.php.
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COVER T M, THOMAS J A. Elements of Information Theory[M]. John Wiley & Sons, Inc., 1991.
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MASSEY JR F J. The Kolmogorov-Smirnov test for goodness of fit[J]. Journal of the American Statistical Association, 1951, 46(253): 68-78.
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LI R, PERNECZKY R, DRZEZGA A, et al. Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment[J]. arXiv preprint arXiv:1512.03955, 2015.
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LECUN Y, CORTES C, BURGES C. THE MNIST DATABASE of handwritten digits[DB/OL]. [2017-08-20]. http://yann.lecun.com/exdb/mnist/.)
|
[1] |
BISHOP C M. Pattern recognition[J]. Machine Learning, 2006, 128: 1-58.
|
[2] |
MURPHY K P. Machine Learning: A Probabilistic Perspective[M]. Cambridge, MA: The MIT Press, 2012.
|
[3] |
BLEI D M, JORDAN M I. Variational inference for Dirichlet process mixtures[J]. Bayesian Analysis, 2006, 1(1): 121-143.
|
[4] |
SHIFFRIN R M, CHANDRAMOULI S H, GRNWALD P D. Bayes factors, relations to minimum description length, and overlapping model classes[J]. Journal of Mathematical Psychology, 2016, 72: 56-77.
|
[5] |
RISSANEN J. Minimum description length principle[M]. John Wiley & Sons, Inc., 1985.
|
[6] |
COVES T F, HRUSCHKA E R. Splitting and merging Gaussian mixture model components: An evolutionary approach[C]// 2011 10th International Conference on Machine Learning and Applications and Workshops. Piscataway, NY, USA: IEEE Press, 2011, 1: 106-111.
|
[7] |
SCRUCCA L. Identifying connected components in Gaussian finite mixture models for clustering[J]. Computational Statistics & Data Analysis, 2016, 93:5-17.
|
[8] |
LI Y, LI L. A greedy merge learning algorithm for Gaussian mixture model[C]// International Symposium on Intelligent Information Technology Application. Piscataway, NY, USA: IEEE Press, 2009:506-509.
|
[9] |
LI Y, LI L. A novel split and merge EM algorithm for Gaussian mixture model[C]// 2009 Fifth International Conference on Natural Computation. Piscataway, NY, USA: IEEE Press, 2009, 6: 479-483.
|
[10] |
COVES T F, HRUSCHKA E R, GHOSH J. Evolving Gaussian mixture models with splitting and merging mutation operators.[J]. Evolutionary Computation, 2016, 24(2):293.
|
[11] |
ZHU R, WANG L, ZHAI C, et al. High-dimensional variance-reduced stochastic gradient expectation-maximization algorithm[J]. Proceedings of Machine Learning Research, 2017, 70: 4180-4188.
|
[12] |
ASUNCION A, NEWMAN D H. UCI machine learning repository[EB/OL]. [2017-08-20]. http://archive.ics.uci.edu/ml/index.php.
|
[13] |
COVER T M, THOMAS J A. Elements of Information Theory[M]. John Wiley & Sons, Inc., 1991.
|
[14] |
MASSEY JR F J. The Kolmogorov-Smirnov test for goodness of fit[J]. Journal of the American Statistical Association, 1951, 46(253): 68-78.
|
[15] |
LI R, PERNECZKY R, DRZEZGA A, et al. Survival analysis, the infinite Gaussian mixture model, FDG-PET and non-imaging data in the prediction of progression from mild cognitive impairment[J]. arXiv preprint arXiv:1512.03955, 2015.
|
[16] |
LECUN Y, CORTES C, BURGES C. THE MNIST DATABASE of handwritten digits[DB/OL]. [2017-08-20]. http://yann.lecun.com/exdb/mnist/.)
|