Mini toolsDefinition
A learning method using both labeled and unlabeled data.
Detailed Explanation
Semi-Supervised Learning combines small amounts of labeled data with large amounts of unlabeled data during training. This approach leverages the unlabeled data to improve learning accuracy when labeled data is scarce or expensive to obtain.
Use Cases
Text classification (with limited labeled documents) image recognition (using unlabeled images) speech analysis bioinformatics (gene function prediction) web content classification.
