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  "Title": "Semi-Supervised Gaussian Mixture Model with a Missing-Data\nMechanism",
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  "Description": "The algorithm of semi-supervised learning is based on\nfinite Gaussian mixture models and includes a mechanism for\nhandling missing data. It aims to fit a g-class Gaussian\nmixture model using maximum likelihood. The algorithm treats\nthe labels of unclassified features as missing data, building\non the framework introduced by Rubin (1976)\n<doi:10.2307/2335739> for missing data analysis. By taking into\naccount the dependencies in the missing pattern, the algorithm\nprovides more information for determining the optimal\nclassifier, as specified by Bayes' rule.",
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  "Maintainer": "Ziyang Lyu <ziyang.lyu@unsw.edu.au>",
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    "rmix",
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        "data.frame"
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      "page": "bayesclassifier",
      "title": "Bayes' rule of allocation",
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      ]
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      "title": "Bootstrap Analysis for gmmsslm",
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        "bootstrap_gmmsslm"
      ]
    },
    {
      "page": "cov2vec",
      "title": "Transform a variance matrix into a vector",
      "topics": [
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      ]
    },
    {
      "page": "discriminant_beta",
      "title": "Discriminant function",
      "topics": [
        "discriminant_beta"
      ]
    },
    {
      "page": "erate",
      "title": "Error rate of the Bayes rule for a g-class Gaussian mixture model",
      "topics": [
        "erate"
      ]
    },
    {
      "page": "errorrate",
      "title": "Error rate of the Bayes rule for two-class Gaussian homoscedastic model",
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        "errorrate"
      ]
    },
    {
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      "title": "Gastrointestinal dataset",
      "topics": [
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      ]
    },
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      "title": "Posterior probability",
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      "topics": [
        "gmmsslm"
      ]
    },
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      "title": "gmmsslmFit Class",
      "topics": [
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    },
    {
      "page": "initialvalue",
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      "topics": [
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      ]
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      "title": "Transfer a list into a vector",
      "topics": [
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      "title": "Log likelihood function formed on the basis of the missing-label indicator",
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