%0 Journal Article %1 zhou2017brief %A Zhou, Zhi-Hua %D 2017 %J National Science Review %K introduction learning machine ml supervised weakly %N 1 %P 44-53 %R 10.1093/nsr/nwx106 %T A brief introduction to weakly supervised learning %U https://doi.org/10.1093/nsr/nwx106 %V 5 %X 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.

@article{zhou2017brief, 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.}}, added-at = {2021-03-12T16:11:25.000+0100}, author = {Zhou, Zhi-Hua}, biburl = {https://www.bibsonomy.org/bibtex/261fe5360dd74d89b019c3ddda4f21b47/jaeschke}, doi = {10.1093/nsr/nwx106}, eprint = {https://academic.oup.com/nsr/article-pdf/5/1/44/31567770/nwx106.pdf}, interhash = {cecf8331d9b456fb65044226c651c297}, intrahash = {61fe5360dd74d89b019c3ddda4f21b47}, issn = {2095-5138}, journal = {National Science Review}, keywords = {introduction learning machine ml supervised weakly}, month = {08}, number = 1, pages = {44-53}, timestamp = {2021-03-12T16:11:25.000+0100}, title = {A brief introduction to weakly supervised learning}, url = {https://doi.org/10.1093/nsr/nwx106}, volume = 5, year = 2017 }