Noisy Label Removal for Partial Multi-Label Learning-Reference-Cited by-同舟云学术

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Noisy Label Removal for Partial Multi-Label Learning

Published:2024-08-24 Issue: Volume:9925 Page:3724-3735
ISSN:
Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Yang Fuchao¹^ORCID,Jia Yuheng²^ORCID,Liu Hui³^ORCID,Dong Yongqiang²^ORCID,Hou Junhui⁴^ORCID

Affiliation:

1. College of Software Engineering, Southeast University, Nanjing, China

2. School of Computer Science and Engineering, Southeast University, Nanjing, China

3. School of Computing & Information Sciences, Saint Francis University, Hong Kong, China

4. Department of Computer Science, City University of Hong Kong, Hong Kong, China

Funder

Natural Science Foundation of Jiangsu Province under Grant

Hong Kong UGC under grant

ational Natural Science Foundation of China under Grant

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671677

Reference37 articles.

1. A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems

2. Learning multi-label scene classification

3. iATC-mISF: a multi-label classifier for predicting the classes of anatomical therapeutic chemicals

4. André Elisseeff and Jason Weston. 2001. A kernel method for multi-labelled classification. In Advances in Neural Information Processing Systems 14. MIT Press, 681--687.

5. Lei Feng, Bo An, and Shuo He. 2019. Collaboration Based Multi-Label Learning. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019. 3550--3557.

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