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Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for
  Multi-Label Classification

Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification

5 November 2019
Guoqiang Wu
Ruobing Zheng
Ying-jie Tian
Dalian Liu
ArXiv (abs)PDFHTML

Papers citing "Joint Ranking SVM and Binary Relevance with Robust Low-Rank Learning for Multi-Label Classification"

7 / 7 papers shown
Title
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM
Improving Similar Case Retrieval Ranking Performance By Revisiting RankSVM
Yuqi Liu
Yan Zheng
AILaw
188
0
0
16 Feb 2025
Multi-Label Learning with Global and Local Label Correlation
Multi-Label Learning with Global and Local Label Correlation
Yue Zhu
James T. Kwok
Zhi Zhou
72
304
0
04 Apr 2017
A Unified View of Multi-Label Performance Measures
A Unified View of Multi-Label Performance Measures
Xi-Zhu Wu
Zhi Zhou
79
218
0
01 Sep 2016
CNN-RNN: A Unified Framework for Multi-label Image Classification
CNN-RNN: A Unified Framework for Multi-label Image Classification
Jiang Wang
Yi Yang
Junhua Mao
Zhiheng Huang
Chang Huang
Wenyuan Xu
SSL
98
1,163
0
15 Apr 2016
Local Rademacher Complexity for Multi-label Learning
Local Rademacher Complexity for Multi-label Learning
Chang Xu
Tongliang Liu
Dacheng Tao
Chao Xu
89
81
0
26 Oct 2014
Large-scale Multi-label Learning with Missing Labels
Large-scale Multi-label Learning with Missing Labels
Hsiang-Fu Yu
Prateek Jain
Purushottam Kar
Inderjit S. Dhillon
MQ
97
492
0
18 Jul 2013
The representer theorem for Hilbert spaces: a necessary and sufficient
  condition
The representer theorem for Hilbert spaces: a necessary and sufficient condition
Francesco Dinuzzo
Bernhard Schölkopf
103
88
0
09 May 2012
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