Semi-supervised learning is a type of machine learning technique that combines elements of supervised learning (the use of labelled data) and unsupervised learning (the use of data without labels). It is used in a wide variety of computer applications, such as image and data analysis, natural language processing, and network inference.
Semi-supervised learning can be broadly considered a synthesis of supervised learning methods, where a small portion of the inputs are labeled, and the remaining data are unlabeled. To this end, semi-supervised learning algorithms make use of both labeled and unlabeled data to identify patterns or structures in a dataset.
The goal of semi-supervised learning is to find a better accuracy than either supervised or unsupervised learning techniques alone. Efficiency and accuracy are improved by exploiting unlabeled data which would otherwise have to be ignored due to the lack of human annotation. Semi-supervised learning also has the benefit of being less costly than traditional supervised learning, since much fewer labeled inputs have to be collected.
Semi-supervised learning is not only useful for improving the accuracy of supervised learning algorithms, but also for knowledge discovery in pattern recognition and machine learning domains. It can be used to build better models in classification, regression, or clustering tasks and can be used to reduce complexity in feature selection and learning models.
Semi-supervised learning has become increasingly important in the era of big data. With such large quantities of unlabeled data available, semi-supervised learning algorithms allow researchers to make use of all available data and produce more accurate models.
