https://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=CC82E9E0A5AA81DC8163412A904747D3?doi=10.1.1.99.9681&rep=rep1&type=pdf

University of Michigan - Ann Arbor

Semi-supervised learning lies somewhere between supervised and unsupervised learning. It takes small amount of labeled data to start learning and then moves on to huge quantity of unlabaled data to develop further.

Semi Supervised learning

Semi-Supervised Learning Literature Survey

D, F. (2017, October 11). Simple explanation of Semi-Supervised Learning and Pseudo Labeling. Retrieved March 14, 2020, from [towardsdatascience.com/simple-explanation-of-semi-supervised-learning-and-pseudo-labeling-c2218e8c769b](https://towardsdatascience.com/simple-explanation-of-semi-supervised-learning-and-pseudo-labeling-c2218e8c769b) 

Simple explanation of Semi-Supervised Learning and Pseudo Labeling (Reference)

Pseudo labeling is a process used in semi-supervised learning, where it combines both supervised and unsupervised techniques in order to train a model. The algorithm is as follows:
1. You use the portion of your data set that is already labeled as the training set to train your model (supervised learning)
2. Once the model is giving you good results, you then use that model with your unlabeled data (unsupervised learning)
    - The model will predict the outcomes of the unlabeled data. We do not know if these are correct, but we have accurate labels after training it with our labeled data
3. We then combine both the original training set with the unlabeled data that was just labeled, training our model on the full data set
Pseudo labeling helps improve the model as well as reduce the error.

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