Semi-supervised learning is a machine learning technique that combines supervised and unsupervised learning to leverage datasets that contain few labelled examples and many unlabelled examples.
In semi-supervised learning, unsupervised learning algorithms are used to extract relevant features and useful representations from the unlabeled data, and then these insights are used to improve the quality of the supervised learning model. The supervised learning model is trained on both the labelled data and the unlabelled data, allowing information from the unlabelled data to be exploited to improve the accuracy of the model.
Semi-supervised learning is particularly useful in applications where the collection of labelled data is costly or difficult, but where a large amount of unlabelled data is available. Semi-supervised learning has been shown to significantly improve the accuracy of machine learning models in speech recognition, computer vision and natural language processing applications.
Companies are increasingly aware of the importance of properly analyzing and managing the huge amount of data they store on a daily basis.
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