Contact Us

Home

Resources

blog

Glossary

Letter

Letter S
Semi-Supervised Learning

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.

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

Your AI Journey Starts Here
Let ORXTRA empower your workflows with AI that’s compliant, efficient, and built for your industry.
Request a Demo

© DXWAND 2025, All Rights Reserved