Semi-supervised learning
What is it ?
It involves a small number of labeled examples and a large number of unlabeled examples. This situation is challenging for either supervised or unsupervised learning to be effective. And this learning algorithm sits between supervised and unsupervised learning.
These algorithms can perform well when we have a very small amount of labeled points and a large amount of unlabeled points.
Ref :
- Resources list - https://machinelearningmastery.com/what-is-semi-supervised-learning/
- Scikit learn - https://scikit-learn.org/stable/modules/semi_supervised.html
Additional Resources
Some examples of good review papers on semi-supervised learning include:
- Semi-Supervised Learning Literature Survey, 2005.
- Introduction to Semi-Supervised Learning, 2009.
- An Overview of Deep Semi-Supervised Learning, 2020.
The scikit-learn Python machine learning library provides a few graph-based semi-supervised learning algorithms that you can try:
The Wikipedia article may also provide some useful links for further reading: