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Last updated : May 19, 2020
Not on the current edition
This blip is not on the current edition of the Radar. If it was on one of the last few editions it is likely that it is still relevant. If the blip is older it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar Understand more
May 2020
Trial ? Worth pursuing. It is important to understand how to build up this capability. Enterprises should try this technology on a project that can handle the risk.

Semi-supervised learning loops are a class of iterative machine-learning workflows that take advantage of the relationships to be found in unlabeled data. These techniques may improve models by combining labeled and unlabeled data sets in various ways. In other cases they compare models trained on different subsets of the data. Unlike either unsupervised learning where a machine infers classes in unlabeled data or supervised techniques where the training set is entirely labeled, semi-supervised techniques take advantage of a small set of labeled data and a much larger set of unlabeled data. Semi-supervised learning is also closely related to active learning techniques where a human is directed to selectively label ambiguous data points. Since expert humans that can accurately label data are a scarce resource and labeling is often the most time-consuming activity in the machine-learning workflow, semi-supervised techniques lower the cost of training and make machine learning feasible for a new class of users. We're also seeing the application of weakly supervised techniques where machine-labeled data is used but is trusted less than the data labeled by humans.

Nov 2019
Assess ? Worth exploring with the goal of understanding how it will affect your enterprise.

Semi-supervised learning loops are a class of iterative machine-learning workflows that take advantage of the relationships to be found in unlabeled data. These techniques may improve models by combining labeled and unlabeled data sets in various ways. In other cases they compare models trained on different subsets of the data. Unlike either unsupervised learning where a machine infers classes in unlabeled data or supervised techniques where the training set is entirely labeled, semi-supervised techniques take advantage of a small set of labeled data and a much larger set of unlabeled data. Semi-supervised learning is also closely related to active learning techniques where a human is directed to selectively label ambiguous data points. Since expert humans that can accurately label data are a scarce resource and labeling is often the most time-consuming activity in the machine-learning workflow, semi-supervised techniques lower the cost of training and make machine learning feasible for a new class of users.

Published : Nov 20, 2019
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