Semi-supervised learning

Semi-supervised learning It is a training approach that uses labelled data and a relatively more number of unlabelled data to build a classifier. This finds applications in cases where it is expensive or infeasible to label large amounts of data while unlabelled data is easily available. For example, in semantic segmentation of satellite images, since it requires each pixel to be labelled, semi-supervised learning has shown that the accuracy of segmentation can be improved with the use of unlabelled data along with a limited number of labelled data. This semi-supervised learning method is found based on a few assumptions. They are: Smoothness/continuity - Two close points x1 and x2 in a high-density region should mean that the respective labels or targets y1 and y2 should remain closer as well. Cluster assumption - The decision boundary should be in a low-density region. Manifold assumption - The high-dimensional data lie on a low-dimensional manifold. Semi-supervised learning can be c