Semi-Supervised Learning is a machine learning paradigm where models learn from a combination of labeled and unlabeled data. It leverages the abundance of unlabeled data with limited labeled data to improve model performance and generalization.
- Glossary > Letter: S
What does "Semi-Supervised Learning" mean?

Use Cases
Text Classification:
Training models with a small labeled dataset and a large amount of unlabeled text data.
Image Recognition:
Enhancing object recognition models with labeled images and unannotated image collections.
Anomaly Detection:
Identifying unusual patterns in data using labeled normal instances and unlabeled data.

Importance
Cost Efficiency:
Reduces the cost and effort of labeling large datasets by utilizing abundant unlabeled data.
Performance Improvement:
Enhances model accuracy and generalization by incorporating additional information from unlabeled data.
Scalability:
Scales well with big data scenarios where labeling resources are limited.

Analogies
Semi-supervised learning is like learning a new language with a combination of formal lessons and immersion. Just as you learn vocabulary and grammar in structured lessons (labeled data) and practice speaking and listening in daily life (unlabeled data), semi-supervised learning combines labeled and unlabeled data to improve learning efficiency
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