16.5: Semi-Supervised Learning

Computational Biology - Genomes, Networks, and Evolution · LibreTexts

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    In some scenarios we have a data set with only a few labeled data points, a large number of unlabeled data points and inherent structure in the data. This type of scenario both clustering and classification do not perform well and a hybrid approach is required. This semi-supervised approach could involve the clustering of data first followed by the classification of the generated clusters.

    This page titled 16.5: Semi-Supervised Learning was last modified on Sun, 03 Jan 2021 23:16:12 GMT and is shared under a CC BY-NC-SA 4.0 license and was authored, remixed, and/or curated by Manolis Kellis et al. (MIT OpenCourseWare) via source content that was edited to the style and standards of the LibreTexts platform.