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  "Title": "Semi-Supervised Classification Methods",
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  "Description": "Provides a collection of self-labeled techniques for\nsemi-supervised classification. In semi-supervised\nclassification, both labeled and unlabeled data are used to\ntrain a classifier. This learning paradigm has obtained\npromising results, specifically in the presence of a reduced\nset of labeled examples. This package implements a collection\nof self-labeled techniques to construct a classification model.\nThis family of techniques enlarges the original labeled set\nusing the most confident predictions to classify unlabeled\ndata. The techniques implemented can be applied to\nclassification problems in several domains by the specification\nof a supervised base classifier. At low ratios of labeled data,\nit can be shown to perform better than classical supervised\nclassifiers.",
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  "License": "GPL (>= 3)",
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  "Date/Publication": "2019-12-16 20:06:18 UTC",
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  "Maintainer": "Christoph Bergmeir <c.bergmeir@decsai.ugr.es>",
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    "democraticCombine",
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      "title": "Time series data set",
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        "data.frame"
      ],
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      "title": "Wine recognition data",
      "object": "wine",
      "file": "wine.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Alcohol",
        "Malic.Acid",
        "Ash",
        "Alcalinity.of.Ash",
        "Magnesium",
        "Total.Phenols",
        "Flavanoids",
        "Nonflavanoid.Phenols",
        "Proanthocyanins",
        "Color.Intensity",
        "Hue",
        "OD280/OD315.of.Diluted.Wines",
        "Proline",
        "Wine"
      ],
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      "table": true,
      "tojson": true
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      "title": "CoBC method",
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      "title": "Combining the hypothesis",
      "topics": [
        "coBCCombine"
      ]
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      ]
    },
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        "coffee"
      ]
    },
    {
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      ]
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