Generally, typical categories of semisupervised learning algorithms include the generative model method, the low-density separation method, the graph-based method, and the heuristic method.
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Open it t of unlabeled data during training when the cost of labeling work may render large, fully labeled training sets infeasible, whereas the acquisition of unlabeled data is relatively inexpensive. <span>Generally, typical categories of semisupervised learning algorithms include the generative model method, the low-density separation method, the graph-based method, and the heuristic method. Some of the popular algorithms for semisupervised learning are summarized in Table S1 in the Supplementary Material <span>