Semi-Supervised Learning Techniques
Understand semi-supervised learning by exploring how it leverages both labeled and unlabeled data. Learn key assumptions like continuity, cluster, and manifold. Discover applications in text classification, speech analysis, and bioinformatics, along with the pseudo-labeling technique used for enhancing training datasets.
We'll cover the following...
Semi-supervised learning
Supervised learning involves the usage of labelled data and unsupervised learning works without labelled data. Semi-supervised learning lies between the two fields. It makes use of both labelled and unlabelled dataset. We looked into the technique of Pseudo-Labeling in the initial lesson of this chapter.
Labeling data is a costly process. The biggest benefit of semi-supervised learning is that it requires a small amount of labelled ...