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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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