Semi-Supervised Learning

Semi-Supervised Learning is a machine learning approach that combines both labeled and unlabeled data for training models. In this framework, a small amount of labeled data—where the output is known—is used alongside a larger amount of unlabeled data—where the output is not known. The primary goal is to leverage the strengths of both types of data to improve model performance, especially when obtaining labeled data is costly or time-consuming.In semi-supervised learning, algorithms attempt to learn the underlying structure of the data by applying techniques that propagate the information from labeled instances to unlabeled ones. This can lead to better generalization and accuracy compared to using only labeled data, particularly in scenarios with limited labeled examples. Common techniques include self-training, co-training, and graph-based methods, which help the model to infer labels for the unlabeled data. Semi-supervised learning is widely used in various applications such as text classification, image recognition, and natural language processing, where obtaining large labeled datasets can be challenging.