+Semi-supervised learning gracefully combines the strengths of [Supervised Learning](/wiki/supervised_learning) with [Unsupervised Learning](/wiki/unsupervised_learning), leveraging small sets of labeled data alongside abundant unlabeled data. This method seeks to build robust models and enhance learning performance, especially when data annotation is scarce or costly.
+## See also
+- [Active Learning](/wiki/active_learning)
+- [Deep Learning](/wiki/deep_learning)
+- [Machine Learning](/wiki/machine_learning)
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