AI GlossarySemi-Supervised LearningENTRY — Machine Learning
[Machine Learning]
Semi-Supervised Learning.
Machine learning approach using a small amount of labeled data with a large amount of unlabeled data.
In-depth explanation
01Semi-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
02EX. 01
Web content classification
EX. 02
Speech recognition with limited transcripts
More in Machine Learning
0301ClassificationPredicting which category or class an input belongs to from a set of predefined categories.02Cross-ValidationA technique to evaluate model performance by training and testing on different subsets of data.03Ensemble LearningCombining multiple models to produce better predictions than any single model.04FeatureAn individual measurable property or characteristic of data used as input to a machine learning model.05Feature EngineeringThe process of using domain knowledge to create new features that improve model performance.06Gradient DescentAn optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
[NEXT] — APPLY THE CONCEPT
Master Semi-Supervised Learning.
Learn how to apply this concept with hands-on projects in our comprehensive AI programs.