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Semi-Supervised Classification

  • Semi-supervised: Uses labeled and unlabeled data to build a classifier
  • Self-training:
    • Build a classifier using the labeled data
    • Use it to label the unlabeled data, and those with the most confident label prediction are added to the set of labeled data
    • Repeat the above process
    • Adv: easy to understand; disadv: may reinforce errors
  • Co-training: Use two or more classifiers to teach each other
    • Each learner uses a mutually independent set of features of each tuple to train a good classifier, say f1
    • Then f1 and f2 are used to predict the class label for unlabeled data X
    • Teach each other: The tuple having the most confident prediction from f1 is added to the set of labeled data for f2, & vice versa
  • Other methods, e.g., joint probability distribution of features and labels

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