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
Classification
Predicting which category or class an input belongs to from a set of predefined categories.
Cross-Validation
A technique to evaluate model performance by training and testing on different subsets of data.
Ensemble Learning
Combining multiple models to produce better predictions than any single model.
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.
Gradient Descent
An optimization algorithm that iteratively adjusts model parameters to minimize the loss function.
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