Semi-supervised learning - notes

Machine learning paradigm where noisy, limited, or imprecise sources are used to provide supervision signal for labeling large amounts of training data in a supervised learning setting (src: Wikipedia)

Application

• Most likely today :
  • Applied in research field : UC Berkeley, UC Riverside, Stanford University, MIT , Google etc

Tools/Framework

• scikit-learn

Implementation

link scikit learn : https://scikit-learn.org/stable/modules/semi_supervised.html#id4

References

[Semi-supervised learning - Wiki] (https://en.wikipedia.org/wiki/Weak_supervision#Semi-supervised_learning) scikit-learn