A brief introduction to weakly supervised learning

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ID: 291642
2017
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Abstract
Supervised learning techniques construct predictive models by learning from a large number of training examples, where each training example has a label indicating its ground-truth output. Though current techniques have achieved great success, it is noteworthy that in many tasks it is difficult to get strong supervision information like fully ground-truth labels due to the high cost of the data-labeling process. Thus, it is desirable for machine-learning techniques to work with weak supervision. This article reviews some research progress of weakly supervised learning, focusing on three typical types of weak supervision: incomplete supervision, where only a subset of training data is given with labels; inexact supervision, where the training data are given with only coarse-grained labels; and inaccurate supervision, where the given labels are not always ground-truth.
Reference Key
openalex_W2746791238 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Zhi‐Hua Zhou
Journal national science review
Year 2017
DOI
10.1093/nsr/nwx106
URL
https://academic.oup.com/nsr/article-pdf/5/1/44/24164438/nwx106.pdf
Keywords Keywords not found

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