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  "Title": "Semi-Supervised Gaussian Mixture Model with a Missing-Data\nMechanism",
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  "Author": "Ziyang Lyu, Daniel Ahfock, Geoffrey J. McLachlan",
  "Maintainer": "Ziyang Lyu <ziyang.lyu@unsw.edu.au>",
  "Description": "The algorithm of semi-supervised learning based on finite\nGaussian mixture models with a missing-data mechanism is\ndesigned for a fitting g-class Gaussian mixture model via\nmaximum likelihood (ML). It is proposed to treat the labels of\nthe unclassified features as missing-data and to introduce a\nframework for their missing as in the pioneering work of Rubin\n(1976) for missing in incomplete data analysis. This dependency\nin the missingness pattern can be leveraged to provide\nadditional information about the optimal classifier as\nspecified by Bayes’ rule.",
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      ]
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      "topics": [
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    },
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      "title": "Fitting Gaussian mixture models",
      "topics": [
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    },
    {
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      ]
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      "topics": [
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      ]
    },
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