%0 Conference Paper %1 sandrock2019combining %A Sandrock, C. %A Herde, M. %A Calma, A. %A Kottke, D. %A Sick, B. %B International Joint Conference on Neural Networks (IJCNN) %D 2019 %I IEEE %K (artificial annotators;confidence annotators;expertise annotators;multiple assignment;self-reported classification;probability;multiple confidence confidences;numerical confidences;{\gamma}- data different estimation;pattern evaluation;graphical evaluation;probabilistic intelligence);maximum levels;{\alpha}-confidence;benevolent likelihood machines majority mining;learning model;nonnormalized models;{\beta}-confidence outputs;intelligent posses quality;class uncertain vote;label weighted %P 1--8 %R 10.1109/IJCNN.2019.8852456 %T Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality

@inproceedings{sandrock2019combining, added-at = {2022-01-07T10:37:59.000+0100}, author = {{Sandrock}, C. and {Herde}, M. and {Calma}, A. and {Kottke}, D. and {Sick}, B.}, biburl = {https://www.bibsonomy.org/bibtex/20d65afb078109abda869d4b027f7bd3f/ies}, booktitle = {International Joint Conference on Neural Networks (IJCNN)}, doi = {10.1109/IJCNN.2019.8852456}, interhash = {714768d5ececcc951fdf412b62425ba3}, intrahash = {0d65afb078109abda869d4b027f7bd3f}, issn = {2161-4393}, keywords = {(artificial annotators;confidence annotators;expertise annotators;multiple assignment;self-reported classification;probability;multiple confidence confidences;numerical confidences;{\gamma}- data different estimation;pattern evaluation;graphical evaluation;probabilistic intelligence);maximum levels;{\alpha}-confidence;benevolent likelihood machines majority mining;learning model;nonnormalized models;{\beta}-confidence outputs;intelligent posses quality;class uncertain vote;label weighted}, pages = {1--8}, publisher = {IEEE}, timestamp = {2022-01-07T10:37:59.000+0100}, title = {Combining Self-reported Confidences from Uncertain Annotators to Improve Label Quality}, year = 2019 }