Definition
Semi-supervised learning uses both labeled and unlabeled data to perform an otherwise supervised learning or unsupervised learning task.
In the former case, there is a distinction between inductive semi-supervised learning and transductive learning. In inductive semi-supervised learning, the learner has both labeled training data \(\{(x_{i},y_{i})\}_{i=1}^{l}\mathop{ \sim }\limits^{ iid}p(\mathbf{x},y)\) and unlabeled training data \(\{\mathbf{x}_{i}\}_{i=l+1}^{l+u}\mathop{ \sim }\limits^{ iid}p(\mathbf{x})\), and learns a predictor \(f : \mathcal{X}\mapsto \mathcal{Y},f \in \mathcal{F}\), where \(\mathcal{F}\) is the hypothesis space. Here \(\mathbf{x} \in \mathcal{X}\) is an input instance, \(y \in \mathcal{Y}\) its target label (discrete for classification or continuous for regression), p(x, y) the unknown joint distribution and p(x ) its marginal, and typically l ≪ u. The goal is to...
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Zhu, X. (2017). Semi-supervised Learning. In: Sammut, C., Webb, G.I. (eds) Encyclopedia of Machine Learning and Data Mining. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-7687-1_749
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