Semi-Supervised Learning

Semi-supervised learning is a machine learning paradigm that combines a small amount of labeled data with a large amount of unlabeled data during training. This approach leverages the strengths of both supervised and unsupervised learning, allowing models to learn from the limited labeled examples while also capturing the underlying structure of the unlabeled data. Common characteristics include the use of algorithms that can effectively exploit the unlabeled data to improve model accuracy and generalization. Semi-supervised learning is particularly useful in scenarios where labeling data is expensive or time-consuming, such as in image classification, natural language processing, and medical diagnosis. By using semi-supervised learning, practitioners can achieve better performance with fewer labeled samples.