Wrapper methods - Co-training
Co-training assumes that (i) features can be split into two sets; (ii) each sub-feature set is sufficient to train a good classifier; (iii) the two sets are conditionally independent given the class.
Initially two separate classifiers are trained with the labeled data, on the two sub-feature sets respectively.
Each classifier then classifies the unlabeled data, and `teaches' the other classifier with the few unlabeled examples (and the predicted labels).
Link to Videos
Wrapper methods for Semi Supervised learning
• Wrapper methods for Semi Supervised learning
Semi supervised Learning: Self-Training
• Semi supervised Learning: Self-Training
Reference:
Blum, A., & Mitchell, T. (1998). Combining labeled and unlabeled data with co-training. In Proceedings of the 11th annual conference on computational learning theory (pp. 92–100). ACM