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- W2131462215 abstract "In this paper, we propose a generative model for self-organizing maps (SOM). Based on this model, we derive three EM-type algorithms for learning SOM, namely, the SOCEM, SOEM, and SODAEM algorithms. SOCEM is derived by using the <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>classification</i> <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>EM</i> (CEM) algorithm to learn the classification likelihood; SOEM is derived by using the EM algorithm to learn the mixture likelihood; and SODAEM is a <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>deterministic</i> <i xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>annealing</i> variant of SOCEM and SOEM. From our experiments on the organizing property of SOM, we observe that SOEM is less sensitive to the initialization of the parameters when using a small-fixed neighborhood than SOCEM, while SODAEM can overcome the initialization problem of SOCEM and SOEM through an annealing process." @default.
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- W2131462215 date "2007-08-01" @default.
- W2131462215 modified "2023-10-18" @default.
- W2131462215 title "CEM, EM, and DAEM Algorithms for Learning Self-Organizing Maps" @default.
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- W2131462215 doi "https://doi.org/10.1109/mlsp.2007.4414336" @default.
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