Semi-supervised learning algorithms

Semi-supervised learning algorithms are a type of machine learning algorithm that combines both labeled and unlabeled data to improve the accuracy of predictions or classifications. In other words, they are given a small amount of labeled data and a large amount of unlabeled data, and they use both types of data to learn a mapping function from input variables (features) to output variables (labels).

The labeled data is used to provide some information about the correct output for certain inputs, while the unlabeled data is used to provide additional information about the structure of the data and the relationships between the inputs and outputs. Semi-supervised learning algorithms can use this additional information to improve their accuracy and reduce overfitting, especially when there is limited labeled data available.

Some examples of popular semi-supervised learning algorithms include self-training, co-training, and multi-view learning.

Semi-supervised learning algorithms are commonly used in applications where labeled data is scarce or expensive to obtain, such as in natural language processing, speech recognition, and image classification, among others.

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