Learning Gaussian mixture with automatic model selection: A comparative study on three Bayesian related approaches
Author:
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Electronic, Optical and Magnetic Materials
Link
http://link.springer.com/content/pdf/10.1007/s11460-011-0153-z.pdf
Reference43 articles.
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2. Redner R, Walker H. Mixture densities, maximum likelihood and the EM algorithm. SIAM Review, 1984, 26(2): 195–239
3. Engel A, den Broeck C P L V. Statistical Mechanics of Learning. New York: Cambridge University Press, 2001
4. Constantinopoulos C, Likas A. Unsupervised learning of Gaussian mixtures based on variational component splitting. IEEE Transactions on Neural Networks, 2007, 18(3): 745–755
5. Verbeek J J, Vlassis N, Krose B. Efficient greedy learning of Gaussian mixture models. Neural Computation, 2003, 15(2): 469–485
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