References
[1]
Tsoumakas
G,
Katakis
I.
Multi-label classification: an overview.
Int J Data Warehousing Min,
2007, 3: 1-13
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Multi-label classification: an overview&author=Tsoumakas G&author=Katakis I&publication_year=2007&journal=Int J Data Warehousing Min&volume=3&pages=1-13
[2]
Geng X, Luo L. Multilabel ranking with inconsistent rankers. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 3742--3747.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Geng X, Luo L. Multilabel ranking with inconsistent rankers. In: Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, Columbus, 2014. 3742--3747&
[3]
Zhou Y, Xue H, Geng X. Emotion distribution recognition from facial expressions. In: Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, 2015. 1247--1250.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Zhou Y, Xue H, Geng X. Emotion distribution recognition from facial expressions. In: Proceedings of the 23rd ACM International Conference on Multimedia, Brisbane, 2015. 1247--1250&
[4]
Geng
X.
Label distribution learning.
IEEE Trans Knowl Data Eng,
2016, 28: 1734-1748
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Label distribution learning&author=Geng X&publication_year=2016&journal=IEEE Trans Knowl Data Eng&volume=28&pages=1734-1748
[5]
Zhou W J, Yu Y, Zhang M L. Binary linear compression for multi-label classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, 2017. 3546--3552.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Zhou W J, Yu Y, Zhang M L. Binary linear compression for multi-label classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, 2017. 3546--3552&
[6]
Gao
B B,
Xing
C,
Xie
C W.
Deep label distribution learning with label ambiguity.
IEEE Trans Image Process,
2017, 26: 2825-2838
CrossRef
PubMed
ADS
arXiv
Google Scholar
http://scholar.google.com/scholar_lookup?title=Deep label distribution learning with label ambiguity&author=Gao B B&author=Xing C&author=Xie C W&publication_year=2017&journal=IEEE Trans Image Process&volume=26&pages=2825-2838
[7]
Wu T F, Lin C J, Weng R C. Probability estimates for multiclass classification by pairwise coupling. J Mach Learn Res, 2004, 5: 975--1005.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Wu T F, Lin C J, Weng R C. Probability estimates for multiclass classification by pairwise coupling. J Mach Learn Res, 2004, 5: 975--1005&
[8]
Lin
H T,
Lin
C J,
Weng
R C.
A note on Platt's probabilistic outputs for support vector machines.
Mach Learn,
2007, 68: 267-276
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=A note on Platt's probabilistic outputs for support vector machines&author=Lin H T&author=Lin C J&author=Weng R C&publication_year=2007&journal=Mach Learn&volume=68&pages=267-276
[9]
Berger A L, Pietra S D, Pietra V J D. A maximum entropy approach to natural language processing. Comput Linguist, 1996, 22: 39--71.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Berger A L, Pietra S D, Pietra V J D. A maximum entropy approach to natural language processing. Comput Linguist, 1996, 22: 39--71&
[10]
Pietra
S D,
Pietra
V D,
Lafferty
J D.
Inducing features of random fields.
IEEE Trans Pattern Anal Machine Intel,
1997, 19: 380-393
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Inducing features of random fields&author=Pietra S D&author=Pietra V D&author=Lafferty J D&publication_year=1997&journal=IEEE Trans Pattern Anal Machine Intel&volume=19&pages=380-393
[11]
Nocedal J, Wright S. Numerical Optimization. 2nd ed. New York: Springer, 2006.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Nocedal J, Wright S. Numerical Optimization. 2nd ed. New York: Springer, 2006&
[12]
Gayar N E, Schwenker F, Palm G. A study of the robustness of KNN classifiers trained using soft labels. In: Proceedings of the 2nd Conference Artificial Neural Networks in Pattern Recognition, Berlin, 2006. 67--80.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Gayar N E, Schwenker F, Palm G. A study of the robustness of KNN classifiers trained using soft labels. In: Proceedings of the 2nd Conference Artificial Neural Networks in Pattern Recognition, Berlin, 2006. 67--80&
[13]
Jiang
X F,
Yi
Z,
Lv
J C.
Fuzzy SVM with a new fuzzy membership function.
Neural Comput Appl,
2006, 15: 268-276
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=Fuzzy SVM with a new fuzzy membership function&author=Jiang X F&author=Yi Z&author=Lv J C&publication_year=2006&journal=Neural Comput Appl&volume=15&pages=268-276
[14]
Lin X T, Chen X W. Mr.KNN: soft relevance for multi-label classification. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, New York, 2010. 349--358.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Lin X T, Chen X W. Mr.KNN: soft relevance for multi-label classification. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, New York, 2010. 349--358&
[15]
Jiang
J Y,
Tsai
S C,
Lee
S J.
FSKNN: multi-label text categorization based on fuzzy similarity and k nearest neighbors.
Expert Syst Appl,
2012, 39: 2813-2821
CrossRef
Google Scholar
http://scholar.google.com/scholar_lookup?title=FSKNN: multi-label text categorization based on fuzzy similarity and k nearest neighbors&author=Jiang J Y&author=Tsai S C&author=Lee S J&publication_year=2012&journal=Expert Syst Appl&volume=39&pages=2813-2821
[16]
Klir J G, Yuan B. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Upper Saddle River: Prentice Hall, 1995.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Klir J G, Yuan B. Fuzzy Sets and Fuzzy Logic: Theory and Applications. Upper Saddle River: Prentice Hall, 1995&
[17]
Li Y K, Zhang M L, Geng X. Leveraging implicit relative labeling-importance information for effective multi-label learning. In: Proceedings of IEEE International Conference on Data Mining, Piscataway, 2015. 251--260.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Li Y K, Zhang M L, Geng X. Leveraging implicit relative labeling-importance information for effective multi-label learning. In: Proceedings of IEEE International Conference on Data Mining, Piscataway, 2015. 251--260&
[18]
Zhu X J, Goldberg A B. Introduction to Semi-Supervised Learning. Boca Raton: Morgan and Claypool Publishers, 2009.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Zhu X J, Goldberg A B. Introduction to Semi-Supervised Learning. Boca Raton: Morgan and Claypool Publishers, 2009&
[19]
Hou P, Geng X, Zhang M L. Multi-label manifold learning. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, Menlo Park, 2016. 1680--1686.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Hou P, Geng X, Zhang M L. Multi-label manifold learning. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence, Menlo Park, 2016. 1680--1686&
[20]
Zhu X J. Semi-supervised learning with graphs. Dissertation for Ph.D. Degree. Pittsburgh: Carnegie Mellon University, 2005.
Google Scholar
http://scholar.google.com/scholar_lookup?title=Zhu X J. Semi-supervised learning with graphs. Dissertation for Ph.D. Degree. Pittsburgh: Carnegie Mellon University, 2005&