Semi-Supervised Learning
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
In-depth explanation
Semi-supervised learning combines labeled and unlabeled data during training. Since labeling data is often expensive and time-consuming, this approach can significantly reduce the labeling effort while still achieving good performance. Techniques include pseudo-labeling, consistency regularization, and self-training.
Examples
Related terms
More in Machine Learning
Supervised Learning
Machine learning approach where models learn from labeled training data to predict outcomes.
Unsupervised Learning
Machine learning approach where models find patterns in data without labeled examples.
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Regression
Predicting a continuous numerical value based on input features.
Feature
An individual measurable property or characteristic of data used as input to a machine learning model.
Feature Engineering
The process of using domain knowledge to create new features that improve model performance.
Master Semi-Supervised Learning.
Learn how to apply this concept with hands-on projects in our comprehensive AI programs.