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Abstract
Sparse data environments challenge traditional machine learning models by limiting the availability of labeled examples. Semi-supervised learning (SSL) offers a promising direction by leveraging both labeled and unlabeled data to improve model generalization. This paper critically evaluates major SSL techniques, comparing their efficacy through empirical analysis and literature synthesis. This study explores consistency regularization, pseudo-labeling, and graph-based methods, examining their theoretical basis and practical impact under sparse conditions. Our results show that appropriate SSL strategies significantly boost performance even in data-scarce settings, thereby offering vital tools for real-world applications with annotation constraints.
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