Semi-Supervised Learning

What Does Semi-Supervised Learning Mean?

Semi-supervised learning is a method used to enable machines to classify both tangible and intangible objects. The objects the machines need to classify or identify could be as varied as inferring the learning patterns of students from classroom videos to drawing inferences from data theft attempts on servers. To learn and infer about objects, machines are provided labeled, shallow information about various types of data based on which the machines need to learn from large, structured and unstructured data they receive regularly.

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Techopedia Explains Semi-Supervised Learning

The little bit of labeled data provided to the systems serve as the starting point for the computer systems. After that, the systems need to accept and learn from large volumes of unlabeled data. However, the labeled data provided may be helpful in classifying the broad type of unlabeled data the system may be receiving. For example, as labeled data, temperatures greater than 104° F should be treated as a case of high fever is given, but in reality, such high temperature may also be because of other complications. It is for the systems to use the basic labeled data and learn more about the large volumes of unlabeled data it receives. Theoretically, semi-supervised learning may be considered a better training method for systems than supervised or unsupervised learning.

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Margaret Rouse
Technology Expert

Margaret is an award-winning technical writer and teacher known for her ability to explain complex technical subjects to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles by the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret's idea of a fun day is helping IT and business professionals learn to speak each other’s highly specialized languages.