Semi-Supervised Learning
Semi-supervised learning is a type of machine learning technique that uses both labeled and unlabeled data to train an algorithm. In this approach, a small amount of labeled data is used to guide the learning process, while a larger amount of unlabeled data is used to improve the accuracy of the model. The goal of semi-supervised learning is to leverage the abundance of unlabeled data to improve the performance of the model, especially in cases where obtaining labeled data is expensive or time-consuming. This approach is particularly useful in natural language processing, computer vision, and speech recognition, where large amounts of unlabeled data are readily available. Semi-supervised learning algorithms include self-training, co-training, and multi-view learning.
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