The problem of semi-supervised induction consists in learning a decision rule from labeled and unlabeled data. This task can be undertaken by discriminative methods, provided that learning criteria are adapted consequently. In this chapter, we motivate the use of entropy regularization as a means to benefit from unlabeled data in the framework of maximum a posteriori estimation. The learning criterion is derived from clearly stated assumptions and can be applied to any smoothly parametrized model of posterior probabilities. The regularization scheme favors low density separation, without any modeling of the density of input features. The contribution of unlabeled data to the learning criterion induces local optima, but this problem can be alleviated by deterministic annealing. For well-behaved models of posterior probabilities, deterministic annealing EM provides a decomposition of the learning problem in a series of concave subproblems. Other approaches to the semi-supervised problem are shown to be close relatives or limiting cases of entropy regularization. A series of experiments illustrates the good behavior of the algorithm in terms of performance and robustness with respect to the violation of the postulated low density separation assumption. The minimum entropy solution benefits from unlabeled data and is able to challenge mixture models and manifold learning in a number of situations.