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Abstract

This chapter proposes a simple taxonomy of probabilistic graphical models for the semi-supervised learning (SSL) problem. It provides some broad classes of algorithms for each of the families and points to specific realizations in the literature. Finally, more detailed light is shed on the family of methods using input-dependent regularization or conditional prior distributions, and parallels to the co-training paradigm are shown. The SSL problem has recently attracted the machine learning community, mainly due to its significant importance in practical applications. The chapter then defines the problem and introduces the notation to be used. It is argued here that SSL is much more a practical than a theoretical problem. A useful SSL technique should be configurable to the specifics of the task in a similar way as Bayesian learning, through the choice of prior and model.