Definition
Semi-supervised learning is a hybrid training paradigm that utilizes a small amount of labeled data alongside a large volume of unlabeled data. The core assumption is that the structure of the unlabeled data can help define decision boundaries more effectively than labeled data alone. Techniques such as self-training, co-training, and graph-based methods are commonly used. This approach is valuable when labeling data is expensive or time-consuming, allowing models to achieve performance close to fully supervised methods with significantly fewer labeled examples.
Summary
A machine learning approach that leverages both labeled and unlabeled data to improve model accuracy and generalization.
Key Concepts
- Labeled data
- Unlabeled data
- Self-training
- Manifold assumption
Use Cases
- Image classification with limited annotations
- Text sentiment analysis with sparse labels
- Medical diagnosis prediction with scarce expert data