Semi-supervised learning algorithms fall between supervised and unsupervised learning algorithms.
They rely upon the assumption that we can exploit the information of the labeled data to improve the result of unsupervised learning algorithms and vice versa.
Being able to use semi-supervised learning algorithms depends on the available data: if we have only labeled data, we can use supervised learning; if we don't have any labeled data, we must go with unsupervised learning methods. However, let's say we have the following:
- Labeled and unlabeled examples
- Examples that are all labeled with the same class
If we have these, then we can use a semi-supervised approach to solve the problem.
The scenario in which we have all the examples labeled with the same class could look like a supervised learning problem, but it isn't.
If the aim of the...
