Semi-Supervised Learning
Semi-Supervised Learning is a machine learning approach that combines a small amount of labeled data with a larger amount of unlabeled data to train a model.
What is Semi-Supervised Learning?
Semi-supervised learning uses labeled examples to guide the learning process while also learning patterns from unlabeled data. This approach is useful when obtaining labeled data is expensive or time-consuming, but large amounts of unlabeled data are readily available.
Why is Semi-Supervised Learning Important?
Semi-supervised learning can reduce the need for extensive manual labeling while improving model performance compared with training on a small labeled dataset alone. It is especially valuable in domains where expert annotation requires significant time, cost, or specialized knowledge.
Common use cases
Semi-Supervised Learning is commonly used in image classification, speech recognition, text classification, medical imaging, fraud detection, and other applications where labeled data is limited.