Label Selection Approach to Learning from Crowds-Reference-Cited by-同舟云学术

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Label Selection Approach to Learning from Crowds

Published:2023-11-26 Issue: Volume: Page:207-221
ISSN:1865-0929
Container-title:Communications in Computer and Information Science
language:
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Author:

Yoshimura Kosuke,Kashima Hisashi

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-99-8132-8_16

Reference32 articles.

1. Albarqouni, S., Baur, C., Achilles, F., Belagiannis, V., Demirci, S., Navab, N.: AggNet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans. Med. Imaging 35, 1313–1321 (2016)

2. Baba, Y., Kashima, H.: Statistical quality estimation for general crowdsourcing tasks. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2013)

3. Braylan, A., Lease, M.: Modeling and aggregation of complex annotations via annotation distances. In: Proceedings of The Web Conference 2020 (2020)

4. Braylan, A., Lease, M.: Aggregating complex annotations via merging and matching. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2021)

5. Chen, P., Sun, H., Yang, Y., Chen, Z.: Adversarial learning from crowds. In: Proceedings of the AAAI Conference on Artificial Intelligence (2022)

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1. Label Selection Approach to Learning from Crowds;Transactions of the Japanese Society for Artificial Intelligence;2024-09-01

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