In the Learning Using Privileged Information (LUPI) model, along with standard training data in the primary space, a teacher supplies a student with additional (privileged) information in the secondary space. The goal of the learner is to find a classifier with a low generalization error in the primary space. One of the major algorithmic tools for learning in the LUPI model is support vector machine plus (SVM+). In this chapter we show two fast algorithms for solving the optimization problem of SVM+. To motivate the usage of SVM+, we show how it can be used to learn from the data that is generated by human computation games.