Scala and Spark for Big Data Analytics

Semisupervised learning

Between supervised and unsupervised learning, there is a small place for semi-supervised learning. In this case, the ML model usually receives an incomplete training signal. More statistically, the ML model receives a training set with some of the target outputs missing. Semi-supervised learning is more or less assumption based and often uses three kinds of assumption algorithms as the learning algorithm for the unlabeled datasets. The following assumptions are used: smoothness, cluster, and manifold. In other words, semi-supervised learning can furthermore be denoted as weakly supervised or a bootstrapping technique for using the hidden wealth of unlabeled examples to enhance the learning from a small amount of labeled ...