Summary
In this chapter, we introduced semi-supervised learning, starting from the scenario and the assumptions needed to justify the approaches. We discussed the importance of the smoothness assumption when working with both supervised and semi-supervised classifiers, in order to guarantee a reasonable generalization ability. Then we introduced the clustering assumption, which is strictly related to the geometry of the datasets, and allows coping with density estimation problems with a strong structural condition.
Finally, we discussed the manifold assumption and its importance in order to avoid the curse of dimensionality.
The chapter continued by introducing a generative and inductive model: Generative Gaussian mixtures, which allow clustering labeled and unlabeled samples starting from the assumption that the prior probabilities are modeled by multivariate Gaussian distributions. We also introduced the concepts of Self-Training and Co-learning. The former...
