Semi-supervised Generative Model Learning

A Semi Supervised Machine Learning model which assume that the distributions take some particular form p(x|y,theta) parameterized by the vector. If these assumptions are incorrect, the unlabeled data may actually decrease the accuracy of the solution relative to what would have been obtained from labeled data alone. However, if the assumptions are correct, then the unlabeled data necessarily improves performance.

References

Weak supervision. Wikipedia. Link.