Semi-Supervised Learning
Date of Addition
22 March 2025
A machine learning approach that combines supervised and unsupervised techniques by training models on a mix of labeled and unlabelled data. This method leverages the structure in unlabelled data to improve generalisation while using limited labeled examples for guidance.
Semi-supervised learning encompasses several methodologies including self-training (using confident predictions on unlabeled data to expand the training set), co-training (using multiple models trained on different feature subsets), multi-view training (using different data representations), and graph-based approaches that propagate labels through similarity networks.
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