Semi-Supervised Learning is a class of supervised learning tasks that also make use of unlabeled data for training – typically a small amount of labeled data with a large amount of unlabelled data. Semi-supervised learning falls between unsupervised learning (without any labeled training data) and supervised learning (with completely labeled training data). Many machine-learning researchers have found that unlabelled data, when used in conjunction with a small amount of labeled data, can produce considerable improvement in learning accuracy. Semi-supervised learning is also of theoretical interest in machine learning and as a model for human learning. Methods of semi-supervise learning include generative methods, low-density separation, graph-based methods, heuristic approaches.
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