Further reading

A solid place to start understanding Semi-supervised learning methods is Xiaojin Zhu's very thorough literature survey, available at http://pages.cs.wisc.edu/~jerryzhu/pub/ssl_survey.pdf.

I also recommend a tutorial by the same author, available in the slide format at http://pages.cs.wisc.edu/~jerryzhu/pub/sslicml07.pdf.

The key paper on Contastive Pessimistic Likelihood Estimation is Loog's 2015 paper http://arxiv.org/abs/1503.00269.

This chapter made a reference to the distinction between generative and discriminative models. A couple of relatively clear explanations of the distinction between generative and discriminative algorithms are provided by Andrew Ng (http://cs229.stanford.edu/notes/cs229-notes2.pdf) and Michael Jordan (http://www.ics.uci.edu/~smyth/courses/cs274/readings/jordan_logistic.pdf).

For readers interested in Bayesian statistics, Allen Downey's book, Think Bayes, is a marvelous introduction (and one of my all-time favorite statistics books...