CCMN: A General Framework for Learning with Class-Conditional Multi-Label Noise.
作者： Sheng-Jun Huang , Ming-Kun Xie
DOI:
关键词: Robustness (computer science) 、 Class (computer programming) 、 Bias of an estimator 、 Machine learning 、 Conditional probability 、 Noise 、 Empirical research 、 Artificial intelligence 、 Focus (optics) 、 Estimator 、 Computer science
摘要: Class-conditional noise commonly exists in machine learning tasks, where the class label is corrupted with a probability depending on its ground-truth. Many research efforts have been made to improve the model robustness against the class-conditional noise. However, they typically focus on the single label case by assuming that only one label is corrupted. In real applications, an instance is usually associated with multiple labels, which could be corrupted simultaneously with their respective conditional probabilities. In this paper, we …
arxiv.org参考文章(41)
Michel Ledoux, Michel Talagrand, Probability in Banach Spaces: Isoperimetry and Processes ,(1991)
Guillaume Stempfel, Liva Ralaivola, Learning SVMs from Sloppily Labeled Data international conference on artificial neural networks. pp. 884- 893 ,(2009) , 10.1007/978-3-642-04274-4_91
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, Xiaogang Wang, Learning from massive noisy labeled data for image classification computer vision and pattern recognition. pp. 2691- 2699 ,(2015) , 10.1109/CVPR.2015.7298885
Inderjit Dhillon, Prateek Jain, Purushottam Kar, Hsiang-Fu Yu, Large-scale Multi-label Learning with Missing Labels international conference on machine learning. pp. 593- 601 ,(2014)
Peter L Bartlett, Michael I Jordan, Jon D McAuliffe, None, Convexity, Classification, and Risk Bounds Journal of the American Statistical Association. ,vol. 101, pp. 138- 156 ,(2006) , 10.1198/016214505000000907
Jesse Read, Bernhard Pfahringer, Geoff Holmes, Eibe Frank, Classifier chains for multi-label classification Machine Learning. ,vol. 85, pp. 333- 359 ,(2011) , 10.1007/S10994-011-5256-5
Tong Zhang, Statistical Analysis of Some Multi-Category Large Margin Classification Methods Journal of Machine Learning Research. ,vol. 5, pp. 1225- 1251 ,(2004)
Dana Angluin, Philip Laird, Learning From Noisy Examples Machine Learning. ,vol. 2, pp. 343- 370 ,(1988) , 10.1023/A:1022873112823
Inderjit S Dhillon, Ambuj Tewari, Pradeep K Ravikumar, Nagarajan Natarajan, Learning with Noisy Labels neural information processing systems. ,vol. 26, pp. 1196- 1204 ,(2013)
Min-Ling Zhang, Zhi-Hua Zhou, A Review On Multi-Label Learning Algorithms IEEE Transactions on Knowledge and Data Engineering. ,vol. 26, pp. 1819- 1837 ,(2014) , 10.1109/TKDE.2013.39