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Semi-Supervised Learning

Curated by: Jerry Xiaojin Zhu


Semi-supervised learning uses both labeled and unlabeled data to improve supervisedlearning. The goal is to learn a predictor that predicts future test data better than the predictor learned from the labeled training data alone. Semi-supervised learning is motivated by its practical value in learning faster, better, and cheaper. In many real world applications, it is relatively easy to acquire a large amount of unlabeled data x. For example, documents can be crawled from the Web, images can be obtained from surveillance cameras, and speech can be collected from broadcast. However, their corresponding labels y for the prediction task, such as sentiment orientation, intrusion detection, and phonetic transcript, often requires slow human annotation and expensive laboratory experiments. This labeling bottleneck results in a scarce of labeled data and a surplus of unlabeled data. Therefore, being able to utilize the surplus unlabeled data is desirable. Common semi-supervised learning methods include generative models, semi-supervised support vector machines, graph Laplacian based methods, co-training, and multiview learning. These methods make different assumptions on the link between the unlabeled data distribution and the classification function. Such assumptions are equivalent to prior domain knowledge, and the success of semi-supervised learning depends to a large degree on the validity of the assumptions.

References:

O. Chapelle and B. Sch{\"o}lkopf and A. Zien. Semi-Supervised Learning. MIT Press, 2006.

A. Subramanya and P. Talukdar. Graph-Based Semi-Supervised Learning. Morgan & Claypool Publishers, 2014.

X. Zhu and A. B. Goldberg. Introduction to Semi-Supervised Learning. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan & Claypool Publishers, 2009.


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