11 Facts About Semi-supervised learning

Semi-supervised learning is an approach to machine learning that combines a small amount of labeled data with a large amount of unlabeled data during training.

Semi-supervised learning falls between unsupervised learning and supervised learning .

Semi-supervised learning is of theoretical interest in machine learning and as a model for human learning.

Semi-supervised learning combines this information to surpass the classification performance that can be obtained either by discarding the unlabeled data and doing supervised learning or by discarding the labels and doing unsupervised learning.

The goal of transductive Semi-supervised learning is to infer the correct labels for the given unlabeled data only.

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Semi-supervised learning algorithms make use of at least one of the following assumptions:.

Transductive Semi-supervised learning framework was formally introduced by Vladimir Vapnik in the 1970s.

Semi-supervised learning has recently become more popular and practically relevant due to the variety of problems for which vast quantities of unlabeled data are available—e.

Whereas support vector machines for supervised Semi-supervised learning seek a decision boundary with maximal margin over the labeled data, the goal of TSVM is a labeling of the unlabeled data such that the decision boundary has maximal margin over all of the data.

Some methods for semi-supervised learning are not intrinsically geared to learning from both unlabeled and labeled data, but instead make use of unlabeled data within a supervised learning framework.

Human responses to formal semi-supervised learning problems have yielded varying conclusions about the degree of influence of the unlabeled data.