semi-supervised learning

Semi-supervised learning is a machine learning approach that combines elements of both supervised and unsupervised learning. It involves using a small set of labeled data along with a larger set of unlabeled data to train models. The labeled data provides initial guidance for the model, while the unlabeled data is used to further refine the model’s learning, often by making assumptions about the structure of the data, such as points that are close together are likely to share a label. This method is particularly useful when labeled data is scarce or expensive to obtain, but there is an abundance of unlabeled data[1][2][3]. Semi-supervised learning can be applied to tasks like classification and regression and is beneficial in fields where labeling is labor-intensive or requires expert knowledge[2].

Citations:

[1] https://www.altexsoft.com/blog/semi-supervised-learning/

[2] https://www.ibm.com/topics/semi-supervised-learning

[3] https://www.geeksforgeeks.org/ml-semi-supervised-learning/

[4] https://www.v7labs.com/blog/semi-supervised-learning-guide

[5] https://blog.roboflow.com/what-is-semi-supervised-learning/

[6] https://machinelearningmastery.com/what-is-semi-supervised-learning/

[7] https://www.datarobot.com/blog/semi-supervised-learning/

[8] https://quiq.com/blog/semi-supervised-learning-explained-with-examples/