Semi-supervised learning is an AI approach that lies between supervised and unsupervised learning. In this method, a model learns from a combination of labeled and unlabeled data. While supervised learning relies solely on labeled data to train models, and unsupervised learning deals with unlabeled data, semi-supervised learning leverages both. This approach acknowledges the scarcity and cost of labeled data while capitalizing on the potential insights within unlabeled data.
Semi-supervised learning is particularly useful when obtaining a large labeled dataset is challenging or expensive. By incorporating unlabeled data, the model can capture more comprehensive patterns and relationships within the data, leading to better generalization and performance. This approach often involves strategies like self-training, where a model makes predictions on unlabeled data, and the confident predictions are then added to the labeled dataset for further training. Semi-supervised learning has applications across domains such as natural language processing, computer vision, and anomaly detection, where labeled data might be limited but large amounts of unlabeled data are available.
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