default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDMin-Ling Zhang
Person information
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
2020 – today
- 2024
[j55]Wei Tang, Weijia Zhang, Min-Ling Zhang:
Multi-instance partial-label learning: towards exploiting dual inexact supervision. Sci. China Inf. Sci. 67(3) (2024)- [j54]Xiang-Ru Yu, Deng-Bao Wang, Min-Ling Zhang
:
Partial label learning with emerging new labels. Mach. Learn. 113(4): 1549-1565 (2024) - [j53]Bin-Bin Jia, Jun-Ying Liu, Min-Ling Zhang:
Towards exploiting linear regression for multi-class/multi-label classification: an empirical analysis. Int. J. Mach. Learn. Cybern. 15(9): 3671-3700 (2024) - [j52]Shuo Zhang, Jianqing Li, Hamido Fujita, Yu-Wen Li, Deng-Bao Wang, Tingting Zhu, Min-Ling Zhang, Chengyu Liu:
Student Loss: Towards the Probability Assumption in Inaccurate Supervision. IEEE Trans. Pattern Anal. Mach. Intell. 46(6): 4460-4475 (2024) - [j51]Yu Zhang, Zhengjie Chen, Tianyu Xu, Junjie Zhao, Siya Mi, Xin Geng, Min-Ling Zhang:
Temporal segment dropout for human action video recognition. Pattern Recognit. 146: 109985 (2024) - [j50]Jian Zhang, Tong Wei, Min-Ling Zhang:
Label-Specific Time-Frequency Energy-Based Neural Network for Instrument Recognition. IEEE Trans. Cybern. 54(11): 7080-7093 (2024) - [j49]Senlin Shu, Deng-Bao Wang, Suqin Yuan, Hongxin Wei, Jiuchuan Jiang, Lei Feng, Min-Ling Zhang:
Multiple-instance Learning from Triplet Comparison Bags. ACM Trans. Knowl. Discov. Data 18(4): 90:1-90:18 (2024) - [j48]Hao Yang, Youzhi Jin, Ziyin Li, Deng-Bao Wang, Xin Geng, Min-Ling Zhang:
Learning From Noisy Labels via Dynamic Loss Thresholding. IEEE Trans. Knowl. Data Eng. 36(11): 6503-6516 (2024) - [c90]Wei-Xuan Bao, Yong Rui, Min-Ling Zhang:
Disentangled Partial Label Learning. AAAI 2024: 11007-11015 - [c89]Yuheng Jia, Xiaorui Peng, Ran Wang, Min-Ling Zhang:
Long-Tailed Partial Label Learning by Head Classifier and Tail Classifier Cooperation. AAAI 2024: 12857-12865 - [c88]Tong Wei, Bo-Lin Wang, Min-Ling Zhang:
EAT: Towards Long-Tailed Out-of-Distribution Detection. AAAI 2024: 15787-15795 - [c87]Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang:
Distilling Reliable Knowledge for Instance-Dependent Partial Label Learning. AAAI 2024: 15888-15896 - [c86]Dong-Dong Wu, Chilin Fu, Weichang Wu, Wenwen Xia, Xiaolu Zhang, Jun Zhou, Min-Ling Zhang:
Efficient Model Stealing Defense with Noise Transition Matrix. CVPR 2024: 24305-24315 - [c85]Zhaofei Wang, Weijia Zhang, Min-Ling Zhang:
Proposal Feature Learning Using Proposal Relations for Weakly Supervised Object Detection. ICME 2024: 1-6 - [c84]Tong Wei, Zhen Mao, Zi-Hao Zhou, Yuanyu Wan, Min-Ling Zhang:
Learning Label Shift Correction for Test-Agnostic Long-Tailed Recognition. ICML 2024 - [c83]Jun-Yi Hang, Min-Ling Zhang:
Binary Decomposition: A Problem Transformation Perspective for Open-Set Semi-Supervised Learning. ICML 2024 - [c82]Deng-Bao Wang, Min-Ling Zhang:
Calibration Bottleneck: Over-compressed Representations are Less Calibratable. ICML 2024 - [c81]Yifan Zhang, Min-Ling Zhang:
Generalization Analysis for Multi-Label Learning. ICML 2024 - [c80]Teng Huang, Bin-Bin Jia, Min-Ling Zhang:
Deep Multi-Dimensional Classification with Pairwise Dimension-Specific Features. IJCAI 2024: 4183-4191 - [c79]Junxiang Mao, Jun-Yi Hang, Min-Ling Zhang:
Learning Label-Specific Multiple Local Metrics for Multi-Label Classification. IJCAI 2024: 4742-4750 - [c78]Wei Tang, Weijia Zhang, Min-Ling Zhang:
Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning. IJCAI 2024: 4973-4981 - [c77]Yi Tang, Yi Gao, Yonggang Luo, Jucheng Yang, Miao Xu, Min-Ling Zhang:
Unlearning from Weakly Supervised Learning. IJCAI 2024: 5000-5008 - [c76]Bo Ye, Kai Gan, Tong Wei, Min-Ling Zhang:
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning. IJCAI 2024: 5362-5370 - [c75]Yinghui Sun, Xingfeng Li, Quansen Sun, Min-Ling Zhang, Zhenwen Ren:
Improved Weighted Tensor Schatten p-Norm for Fast Multi-view Graph Clustering. ACM Multimedia 2024: 1427-1436 - [i24]Kai Gan, Tong Wei, Min-Ling Zhang:
Boosting Consistency in Dual Training for Long-Tailed Semi-Supervised Learning. CoRR abs/2406.13187 (2024) - [i23]Xin Liu, Weijia Zhang, Min-Ling Zhang:
Attention Is Not What You Need: Revisiting Multi-Instance Learning for Whole Slide Image Classification. CoRR abs/2408.09449 (2024) - [i22]Wei Tang, Weijia Zhang, Min-Ling Zhang:
Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning. CoRR abs/2408.14369 (2024) - [i21]Tong Wei, Hao-Tian Li, Chun-Shu Li, Jiang-Xin Shi, Yu-Feng Li, Min-Ling Zhang:
Vision-Language Models are Strong Noisy Label Detectors. CoRR abs/2409.19696 (2024) - 2023
- [j47]Bin-Bin Jia, Jun-Ying Liu, Jun-Yi Hang, Min-Ling Zhang:
Learning label-specific features for decomposition-based multi-class classification. Frontiers Comput. Sci. 17(6): 176348 (2023) - [j46]Ning Xu, Jun Shu, RenYi Zheng, Xin Geng, Deyu Meng, Min-Ling Zhang:
Variational Label Enhancement. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 6537-6551 (2023) - [j45]Bing-Qing Liu, Bin-Bin Jia, Min-Ling Zhang:
Towards Enabling Binary Decomposition for Partial Multi-Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(11): 13203-13217 (2023) - [j44]Bin-Bin Jia, Min-Ling Zhang:
Multi-dimensional multi-label classification: Towards encompassing heterogeneous label spaces and multi-label annotations. Pattern Recognit. 138: 109357 (2023) - [j43]Bin-Bin Jia, Min-Ling Zhang:
Multi-Dimensional Classification via Decomposed Label Encoding. IEEE Trans. Knowl. Data Eng. 35(2): 1844-1856 (2023) - [c74]Xin Cheng, Deng-Bao Wang, Lei Feng, Min-Ling Zhang, Bo An:
Partial-Label Regression. AAAI 2023: 7140-7147 - [c73]Ruo-Jing Dong, Jun-Yi Hang, Tong Wei, Min-Ling Zhang:
Can Label-Specific Features Help Partial-Label Learning? AAAI 2023: 7432-7440 - [c72]Deng-Bao Wang, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Min-Ling Zhang:
On the Pitfall of Mixup for Uncertainty Calibration. CVPR 2023: 7609-7618 - [c71]Yifan Zhang, Min-Ling Zhang:
Nearly-tight Bounds for Deep Kernel Learning. ICML 2023: 41861-41879 - [c70]Yi Gao, Miao Xu, Min-Ling Zhang:
Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning. IJCAI 2023: 3732-3740 - [c69]Teng Huang, Bin-Bin Jia, Min-Ling Zhang:
Progressive Label Propagation for Semi-Supervised Multi-Dimensional Classification. IJCAI 2023: 3821-3829 - [c68]Hao-Tian Li, Tong Wei, Hao Yang, Kun Hu, Chong Peng, Li-Bo Sun, Xun-Liang Cai, Min-Ling Zhang:
Stochastic Feature Averaging for Learning with Long-Tailed Noisy Labels. IJCAI 2023: 3902-3910 - [c67]Junxiang Mao, Wei Wang, Min-Ling Zhang:
Label Specific Multi-Semantics Metric Learning for Multi-Label Classification: Global Consideration Helps. IJCAI 2023: 4055-4063 - [c66]Yuheng Jia, Chongjie Si, Min-Ling Zhang:
Complementary Classifier Induced Partial Label Learning. KDD 2023: 974-983 - [c65]Jun-Yi Hang, Min-Ling Zhang:
Partial Multi-Label Learning with Probabilistic Graphical Disambiguation. NeurIPS 2023 - [c64]Wei Tang, Weijia Zhang, Min-Ling Zhang:
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning. NeurIPS 2023 - [c63]Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. NeurIPS 2023 - [i20]Yi Gao, Miao Xu, Min-Ling Zhang:
Complementary to Multiple Labels: A Correlation-Aware Correction Approach. CoRR abs/2302.12987 (2023) - [i19]Zhaofei Wang, Weijia Zhang, Min-Ling Zhang:
Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection. CoRR abs/2303.14999 (2023) - [i18]Hanwen Deng, Weijia Zhang, Min-Ling Zhang:
Rethinking the Value of Labels for Instance-Dependent Label Noise Learning. CoRR abs/2305.06247 (2023) - [i17]Yuheng Jia, Chongjie Si, Min-Ling Zhang:
Complementary Classifier Induced Partial Label Learning. CoRR abs/2305.09897 (2023) - [i16]Wei Tang, Weijia Zhang, Min-Ling Zhang:
Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning. CoRR abs/2305.16912 (2023) - [i15]Xin Cheng, Deng-Bao Wang, Lei Feng, Min-Ling Zhang, Bo An:
Partial-Label Regression. CoRR abs/2306.08968 (2023) - [i14]Yu Shi, Dong-Dong Wu, Xin Geng, Min-Ling Zhang:
Robust Representation Learning for Unreliable Partial Label Learning. CoRR abs/2308.16718 (2023) - [i13]Bo Ye, Kai Gan, Tong Wei, Min-Ling Zhang:
Bridging the Gap: Learning Pace Synchronization for Open-World Semi-Supervised Learning. CoRR abs/2309.11930 (2023) - [i12]Wei Wang, Lei Feng, Yuchen Jiang, Gang Niu, Min-Ling Zhang, Masashi Sugiyama:
Binary Classification with Confidence Difference. CoRR abs/2310.05632 (2023) - [i11]Tong Wei, Bo-Lin Wang, Min-Ling Zhang:
EAT: Towards Long-Tailed Out-of-Distribution Detection. CoRR abs/2312.08939 (2023) - 2022
- [j42]Yi-Bo Wang, Jun-Yi Hang, Min-Ling Zhang:
Stable Label-Specific Features Generation for Multi-Label Learning via Mixture-Based Clustering Ensemble. IEEE CAA J. Autom. Sinica 9(7): 1248-1261 (2022) - [j41]Bin-Bin Jia, Min-Ling Zhang:
Multi-dimensional Classification via Selective Feature Augmentation. Int. J. Autom. Comput. 19(1): 38-51 (2022) - [j40]Min-Ling Zhang, Xiu-Shen Wei, Gao Huang:
Preface. J. Comput. Sci. Technol. 37(3): 505-506 (2022) - [j39]Ze-Bang Yu, Min-Ling Zhang:
Multi-Label Classification With Label-Specific Feature Generation: A Wrapped Approach. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 5199-5210 (2022) - [j38]Deng-Bao Wang, Min-Ling Zhang, Li Li:
Adaptive Graph Guided Disambiguation for Partial Label Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 8796-8811 (2022) - [j37]Jun-Yi Hang, Min-Ling Zhang:
Collaborative Learning of Label Semantics and Deep Label-Specific Features for Multi-Label Classification. IEEE Trans. Pattern Anal. Mach. Intell. 44(12): 9860-9871 (2022) - [j36]Bin-Bin Jia, Min-Ling Zhang:
Decomposition-Based Classifier Chains for Multi-Dimensional Classification. IEEE Trans. Artif. Intell. 3(2): 176-191 (2022) - [j35]Min-Ling Zhang, Yu-Kun Li, Hao Yang, Xu-Ying Liu:
Towards Class-Imbalance Aware Multi-Label Learning. IEEE Trans. Cybern. 52(6): 4459-4471 (2022) - [j34]Yao Zhang, Wenping Fan, Qichen Hao, Xinya Wu, Min-Ling Zhang:
CAFE and SOUP: Toward Adaptive VDI Workload Prediction. ACM Trans. Intell. Syst. Technol. 13(6): 94:1-94:28 (2022) - [j33]Min-Ling Zhang, Jun-Peng Fang, Yi-Bo Wang:
BiLabel-Specific Features for Multi-Label Classification. ACM Trans. Knowl. Discov. Data 16(1): 18:1-18:23 (2022) - [j32]Min-Ling Zhang, Jing-Han Wu, Wei-Xuan Bao:
Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction. ACM Trans. Knowl. Discov. Data 16(4): 72:1-72:18 (2022) - [j31]Bin-Bin Jia, Min-Ling Zhang:
Maximum Margin Multi-Dimensional Classification. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7185-7198 (2022) - [c62]Jun-Yi Hang, Min-Ling Zhang, Yanghe Feng, Xiaocheng Song:
End-to-End Probabilistic Label-Specific Feature Learning for Multi-Label Classification. AAAI 2022: 6847-6855 - [c61]Jun-Yi Hang, Min-Ling Zhang:
Dual Perspective of Label-Specific Feature Learning for Multi-Label Classification. ICML 2022: 8375-8386 - [c60]Dong-Dong Wu, Deng-Bao Wang, Min-Ling Zhang:
Revisiting Consistency Regularization for Deep Partial Label Learning. ICML 2022: 24212-24225 - [c59]Yu-Xuan Shi, Deng-Bao Wang, Min-Ling Zhang:
Partial Label Learning with Gradually Induced Error-Correction Output Codes. ICONIP (1) 2022: 200-211 - [c58]Wei-Xuan Bao, Jun-Yi Hang, Min-Ling Zhang:
Submodular Feature Selection for Partial Label Learning. KDD 2022: 26-34 - [c57]Wei Wang, Min-Ling Zhang:
Partial Label Learning with Discrimination Augmentation. KDD 2022: 1920-1928 - [c56]Zhuying Li, Si Cheng, Wei Wang, Min-Ling Zhang:
(Re-)connecting with Nature in Urban Life: Engaging with Wildlife via AI-powered Wearables. MobileHCI (Adjunct) 2022: 14:1-14:5 - [c55]Weijia Zhang, Xuanhui Zhang, Hanwen Deng, Min-Ling Zhang:
Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization. NeurIPS 2022 - [c54]Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang:
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement. NeurIPS 2022 - [c53]Tong Wei, Jiang-Xin Shi, Yufeng Li, Min-Ling Zhang:
Prototypical Classifier for Robust Class-Imbalanced Learning. PAKDD (2) 2022: 44-57 - [i10]Weijia Zhang, Xuanhui Zhang, Hanwen Deng, Min-Ling Zhang:
Towards Learning Causal Representations from Multi-Instance Bags. CoRR abs/2202.12570 (2022) - [i9]Ning Xu, Congyu Qiao, Jiaqi Lv, Xin Geng, Min-Ling Zhang:
One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement. CoRR abs/2206.00517 (2022) - [i8]Chongjie Si, Yuheng Jia, Ran Wang, Min-Ling Zhang, Yanghe Feng, Chongxiao Qu:
Multi-label Classification with High-rank and High-order Label Correlations. CoRR abs/2207.04197 (2022) - [i7]Tong Wei, Zhen Mao, Jiang-Xin Shi, Yu-Feng Li, Min-Ling Zhang:
A Survey on Extreme Multi-label Learning. CoRR abs/2210.03968 (2022) - [i6]Wei Tang, Weijia Zhang, Min-Ling Zhang:
Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision. CoRR abs/2212.08997 (2022) - 2021
- [j30]Yan-Ping Sun, Min-Ling Zhang:
Compositional metric learning for multi-label classification. Frontiers Comput. Sci. 15(5): 155320 (2021) - [j29]Min-Ling Zhang, Sheng-Jun Huang, Mingsheng Long:
Preface. J. Comput. Sci. Technol. 36(3): 588-589 (2021) - [j28]Min-Ling Zhang, Jun-Peng Fang:
Partial Multi-Label Learning via Credible Label Elicitation. IEEE Trans. Pattern Anal. Mach. Intell. 43(10): 3587-3599 (2021) - [j27]Min-Ling Zhang, Qian-Wen Zhang, Jun-Peng Fang, Yu-Kun Li, Xin Geng:
Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-Label Learning. IEEE Trans. Knowl. Data Eng. 33(5): 2057-2070 (2021) - [c52]Deng-Bao Wang, Yong Wen, Lujia Pan, Min-Ling Zhang:
Learning from Noisy Labels with Complementary Loss Functions. AAAI 2021: 10111-10119 - [c51]Zhen-Ru Zhang, Qian-Wen Zhang, Yunbo Cao, Min-Ling Zhang:
Exploiting Unlabeled Data via Partial Label Assignment for Multi-Class Semi-Supervised Learning. AAAI 2021: 10973-10980 - [c50]Yi Gao, Min-Ling Zhang:
Discriminative Complementary-Label Learning with Weighted Loss. ICML 2021: 3587-3597 - [c49]Bin-Bin Jia, Min-Ling Zhang:
Multi-Dimensional Classification via Sparse Label Encoding. ICML 2021: 4917-4926 - [c48]Wen-Ping Fan, Yao Zhang, Qichen Hao, Xinya Wu, Min-Ling Zhang:
BAMBOO: A Multi-instance Multi-label Approach Towards VDI User Logon Behavior Modeling. IJCAI 2021: 2367-2373 - [c47]Deng-Bao Wang, Lei Feng, Min-Ling Zhang:
Learning from Complementary Labels via Partial-Output Consistency Regularization. IJCAI 2021: 3075-3081 - [c46]Qian-Wen Zhang, Ximing Zhang, Zhao Yan, Ruifang Liu, Yunbo Cao, Min-Ling Zhang:
Correlation-Guided Representation for Multi-Label Text Classification. IJCAI 2021: 3363-3369 - [c45]Wei-Xuan Bao, Jun-Yi Hang, Min-Ling Zhang:
Partial Label Dimensionality Reduction via Confidence-Based Dependence Maximization. KDD 2021: 46-54 - [c44]Jiachen Wang, Dazhen Deng, Xiao Xie, Xinhuan Shu, Yu-Xuan Huang, Le-Wen Cai, Hui Zhang, Min-Ling Zhang, Zhi-Hua Zhou, Yingcai Wu:
Tac-Valuer: Knowledge-based Stroke Evaluation in Table Tennis. KDD 2021: 3688-3696 - [c43]Deng-Bao Wang, Lei Feng, Min-Ling Zhang:
Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence. NeurIPS 2021: 11809-11820 - [c42]Ning Xu, Congyu Qiao, Xin Geng, Min-Ling Zhang:
Instance-Dependent Partial Label Learning. NeurIPS 2021: 27119-27130 - [i5]Hao Yang, Youzhi Jin, Ziyin Li, Deng-Bao Wang, Lei Miao, Xin Geng, Min-Ling Zhang:
Learning from Noisy Labels via Dynamic Loss Thresholding. CoRR abs/2104.02570 (2021) - [i4]Tong Wei, Jiang-Xin Shi, Yu-Feng Li, Min-Ling Zhang:
Prototypical Classifier for Robust Class-Imbalanced Learning. CoRR abs/2110.11553 (2021) - [i3]Ning Xu, Congyu Qiao, Xin Geng, Min-Ling Zhang:
Instance-Dependent Partial Label Learning. CoRR abs/2110.12911 (2021) - 2020
- [j26]Bin-Bin Jia, Min-Ling Zhang:
Multi-dimensional classification via stacked dependency exploitation. Sci. China Inf. Sci. 63(12) (2020) - [j25]Min-Ling Zhang, Yu-Feng Li, Qi Liu:
Preface. J. Comput. Sci. Technol. 35(2): 231-233 (2020) - [j24]Yu Zhang, Yin Wang, Xu-Ying Liu, Siya Mi, Min-Ling Zhang:
Large-scale multi-label classification using unknown streaming images. Pattern Recognit. 99 (2020) - [j23]Bin-Bin Jia, Min-Ling Zhang:
Multi-dimensional classification via kNN feature augmentation. Pattern Recognit. 106: 107423 (2020) - [c41]Ze-Sen Chen, Xuan Wu, Qing-Guo Chen, Yao Hu, Min-Ling Zhang:
Multi-View Partial Multi-Label Learning with Graph-Based Disambiguation. AAAI 2020: 3553-3560 - [c40]Bin-Bin Jia, Min-Ling Zhang:
Maximum Margin Multi-Dimensional Classification. AAAI 2020: 4312-4319 - [c39]Bin-Bin Jia, Min-Ling Zhang:
Md-knn: An Instance-based Approach for Multi-Dimensional Classification. ICPR 2020: 126-133 - [c38]Jing-Han Wu, Xuan Wu, Qing-Guo Chen, Yao Hu, Min-Ling Zhang:
Feature-Induced Manifold Disambiguation for Multi-View Partial Multi-label Learning. KDD 2020: 557-565 - [c37]Wei Wang, Min-Ling Zhang:
Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization. NeurIPS 2020
2010 – 2019
- 2019
- [j22]Xu-Ying Liu, Sheng-Tao Wang, Min-Ling Zhang:
Transfer synthetic over-sampling for class-imbalance learning with limited minority class data. Frontiers Comput. Sci. 13(5): 996-1009 (2019) - [j21]Ming Huang, Fuzhen Zhuang, Xiao Zhang, Xiang Ao, Zhengyu Niu, Min-Ling Zhang, Qing He:
Supervised representation learning for multi-label classification. Mach. Learn. 108(5): 747-763 (2019) - [j20]Yan Cui, Jielin Jiang, Zuojin Hu, Xiaoyan Jiang, Wuxia Yan, Min-Ling Zhang:
Neighborhood kinship preserving hashing for supervised learning. Signal Process. Image Commun. 76: 31-40 (2019) - [c36]Jun-Peng Fang, Min-Ling Zhang:
Partial Multi-Label Learning via Credible Label Elicitation. AAAI 2019: 3518-3525 - [c35]Bin-Bin Jia, Min-Ling Zhang:
Multi-Dimensional Classification via kNN Feature Augmentation. AAAI 2019: 3975-3982 - [c34]Yao Zhang, Wen-Ping Fan, Xuan Wu, Hua Chen, Bin-Yang Li, Min-Ling Zhang:
CAFE: Adaptive VDI Workload Prediction with Multi-Grained Features. AAAI 2019: 5821-5828 - [c33]Ze-Sen Chen, Min-Ling Zhang:
Multi-Label Learning with Regularization Enriched Label-Specific Features. ACML 2019: 411-424 - [c32]Xuan Wu, Qing-Guo Chen, Yao Hu, Dengbao Wang, Xiaodong Chang, Xiaobo Wang, Min-Ling Zhang:
Multi-View Multi-Label Learning with View-Specific Information Extraction. IJCAI 2019: 3884-3890 - [c31]Deng-Bao Wang, Li Li, Min-Ling Zhang:
Adaptive Graph Guided Disambiguation for Partial Label Learning. KDD 2019: 83-91 - [c30]Jing-Han Wu, Min-Ling Zhang:
Disambiguation Enabled Linear Discriminant Analysis for Partial Label Dimensionality Reduction. KDD 2019: 416-424 - [e6]Qiang Yang, Zhi-Hua Zhou, Zhiguo Gong, Min-Ling Zhang, Sheng-Jun Huang:
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part I. Lecture Notes in Computer Science 11439, Springer 2019, ISBN 978-3-030-16147-7 [contents] - [e5]Qiang Yang, Zhi-Hua Zhou, Zhiguo Gong, Min-Ling Zhang, Sheng-Jun Huang:
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part II. Lecture Notes in Computer Science 11440, Springer 2019, ISBN 978-3-030-16144-6 [contents] - [e4]Qiang Yang, Zhi-Hua Zhou, Zhiguo Gong, Min-Ling Zhang, Sheng-Jun Huang:
Advances in Knowledge Discovery and Data Mining - 23rd Pacific-Asia Conference, PAKDD 2019, Macau, China, April 14-17, 2019, Proceedings, Part III. Lecture Notes in Computer Science 11441, Springer 2019, ISBN 978-3-030-16141-5 [contents] - 2018
- [j19]Min-Ling Zhang, Yu-Kun Li, Xu-Ying Liu, Xin Geng:
Binary relevance for multi-label learning: an overview. Frontiers Comput. Sci. 12(2): 191-202 (2018) - [j18]Deyu Zhou, Zhikai Zhang, Min-Ling Zhang, Yulan He:
Weakly Supervised POS Tagging without Disambiguation. ACM Trans. Asian Low Resour. Lang. Inf. Process. 17(4): 35:1-35:19 (2018) - [c29]Qian-Wen Zhang, Yun Zhong, Min-Ling Zhang:
Feature-Induced Labeling Information Enrichment for Multi-Label Learning. AAAI 2018: 4446-4453 - [c28]Ke Shang, Hisao Ishibuchi, Min-Ling Zhang, Yiping Liu:
A new R2 indicator for better hypervolume approximation. GECCO 2018: 745-752 - [c27]Si-Yu Ding, Xu-Ying Liu, Min-Ling Zhang:
Imbalanced Augmented Class Learning with Unlabeled Data by Label Confidence Propagation. ICDM 2018: 79-88 - [c26]Xuan Wu, Min-Ling Zhang:
Towards Enabling Binary Decomposition for Partial Label Learning. IJCAI 2018: 2868-2874 - [c25]Jing Wang, Min-Ling Zhang:
Towards Mitigating the Class-Imbalance Problem for Partial Label Learning. KDD 2018: 2427-2436 - 2017
- [j17]Fei Yu, Min-Ling Zhang:
Maximum margin partial label learning. Mach. Learn. 106(4): 573-593 (2017) - [j16]Min-Ling Zhang, Fei Yu, Cai-Zhi Tang:
Disambiguation-Free Partial Label Learning. IEEE Trans. Knowl. Data Eng. 29(10): 2155-2167 (2017) - [c24]Cai-Zhi Tang, Min-Ling Zhang:
Confidence-Rated Discriminative Partial Label Learning. AAAI 2017: 2611-2617 - [c23]Wang Zhan, Min-Ling Zhang:
Multi-label Learning with Label-Specific Features via Clustering Ensemble. DSAA 2017: 129-136 - [c22]Wen-Ji Zhou, Yang Yu, Min-Ling Zhang:
Binary Linear Compression for Multi-label Classification. IJCAI 2017: 3546-3552 - [c21]Wang Zhan, Min-Ling Zhang:
Inductive Semi-supervised Multi-Label Learning with Co-Training. KDD 2017: 1305-1314 - [e3]Min-Ling Zhang, Yung-Kyun Noh:
Proceedings of The 9th Asian Conference on Machine Learning, ACML 2017, Seoul, Korea, November 15-17, 2017. Proceedings of Machine Learning Research 77, PMLR 2017 [contents] - [e2]Hujun Yin, Yang Gao, Songcan Chen, Yimin Wen, Guoyong Cai, Tianlong Gu, Junping Du, Antonio J. Tallón-Ballesteros, Min-Ling Zhang:
Intelligent Data Engineering and Automated Learning - IDEAL 2017 - 18th International Conference, Guilin, China, October 30 - November 1, 2017, Proceedings. Lecture Notes in Computer Science 10585, Springer 2017, ISBN 978-3-319-68934-0 [contents] - [r1]Zhi-Hua Zhou, Min-Ling Zhang:
Multi-label Learning. Encyclopedia of Machine Learning and Data Mining 2017: 875-881 - 2016
- [c20]Peng Hou, Xin Geng, Min-Ling Zhang:
Multi-Label Manifold Learning. AAAI 2016: 1680-1686 - [c19]Min-Ling Zhang, Bin-Bin Zhou, Xu-Ying Liu:
Partial Label Learning via Feature-Aware Disambiguation. KDD 2016: 1335-1344 - [e1]Richard Booth, Min-Ling Zhang:
PRICAI 2016: Trends in Artificial Intelligence - 14th Pacific Rim International Conference on Artificial Intelligence, Phuket, Thailand, August 22-26, 2016, Proceedings. Lecture Notes in Computer Science 9810, Springer 2016, ISBN 978-3-319-42910-6 [contents] - 2015
- [j15]Min-Ling Zhang, Lei Wu:
Lift: Multi-Label Learning with Label-Specific Features. IEEE Trans. Pattern Anal. Mach. Intell. 37(1): 107-120 (2015) - [c18]Fei Yu, Min-Ling Zhang:
Maximum Margin Partial Label Learning. ACML 2015: 96-111 - [c17]Yu-Kun Li, Min-Ling Zhang, Xin Geng:
Leveraging Implicit Relative Labeling-Importance Information for Effective Multi-label Learning. ICDM 2015: 251-260 - [c16]Min-Ling Zhang, Yu-Kun Li, Xu-Ying Liu:
Towards Class-Imbalance Aware Multi-Label Learning. IJCAI 2015: 4041-4047 - [c15]Min-Ling Zhang, Fei Yu:
Solving the Partial Label Learning Problem: An Instance-Based Approach. IJCAI 2015: 4048-4054 - 2014
- [j14]Min-Ling Zhang, Zhi-Hua Zhou:
A Review on Multi-Label Learning Algorithms. IEEE Trans. Knowl. Data Eng. 26(8): 1819-1837 (2014) - [c14]Yu-Kun Li, Min-Ling Zhang:
Enhancing Binary Relevance for Multi-label Learning with Controlled Label Correlations Exploitation. PRICAI 2014: 91-103 - [c13]Min-Ling Zhang:
Disambiguation-Free Partial Label Learning. SDM 2014: 37-45 - 2013
- [j13]Min-Ling Zhang, Zhi-Hua Zhou:
Exploiting unlabeled data to enhance ensemble diversity. Data Min. Knowl. Discov. 26(1): 98-129 (2013) - [c12]Le Wu, Min-Ling Zhang:
Multi-Label Classification with Unlabeled Data: An Inductive Approach. ACML 2013: 197-212 - 2012
- [j12]Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang, Yufeng Li:
Multi-instance multi-label learning. Artif. Intell. 176(1): 2291-2320 (2012) - [j11]Grigorios Tsoumakas, Min-Ling Zhang, Zhi-Hua Zhou:
Introduction to the special issue on learning from multi-label data. Mach. Learn. 88(1-2): 1-4 (2012) - 2011
- [j10]Min-Ling Zhang, Zhi-Hua Zhou:
CoTrade: Confident Co-Training With Data Editing. IEEE Trans. Syst. Man Cybern. Part B 41(6): 1612-1626 (2011) - [c11]Min-Ling Zhang:
LIFT: Multi-Label Learning with Label-Specific Features. IJCAI 2011: 1609-1614 - 2010
- [c10]Min-Ling Zhang, Zhi-Hua Zhou:
Exploiting Unlabeled Data to Enhance Ensemble Diversity. ICDM 2010: 619-628 - [c9]Min-Ling Zhang:
A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm. ICTAI (2) 2010: 207-212 - [c8]Min-Ling Zhang, Kun Zhang:
Multi-label learning by exploiting label dependency. KDD 2010: 999-1008
2000 – 2009
- 2009
- [j9]Min-Ling Zhang, Zhi-Hua Zhou:
Multi-instance clustering with applications to multi-instance prediction. Appl. Intell. 31(1): 47-68 (2009) - [j8]Min-Ling Zhang, Zhijian Wang:
MIMLRBF: RBF neural networks for multi-instance multi-label learning. Neurocomputing 72(16-18): 3951-3956 (2009) - [j7]Min-Ling Zhang, José María Peña Sánchez, Víctor Robles:
Feature selection for multi-label naive Bayes classification. Inf. Sci. 179(19): 3218-3229 (2009) - [j6]Min-Ling Zhang:
Ml-rbf : RBF Neural Networks for Multi-Label Learning. Neural Process. Lett. 29(2): 61-74 (2009) - [i2]Min-Ling Zhang, Zhi-Hua Zhou:
Classifier Ensemble with Unlabeled Data. CoRR abs/0909.3593 (2009) - 2008
- [c7]Min-Ling Zhang, Zhi-Hua Zhou:
M3MIML: A Maximum Margin Method for Multi-instance Multi-label Learning. ICDM 2008: 688-697 - [i1]Zhi-Hua Zhou, Min-Ling Zhang, Sheng-Jun Huang, Yufeng Li:
MIML: A Framework for Learning with Ambiguous Objects. CoRR abs/0808.3231 (2008) - 2007
- [j5]Zhi-Hua Zhou, Min-Ling Zhang:
Solving multi-instance problems with classifier ensemble based on constructive clustering. Knowl. Inf. Syst. 11(2): 155-170 (2007) - [j4]Min-Ling Zhang, Zhi-Hua Zhou:
ML-KNN: A lazy learning approach to multi-label learning. Pattern Recognit. 40(7): 2038-2048 (2007) - [c6]Min-Ling Zhang, Zhi-Hua Zhou:
Multi-Label Learning by Instance Differentiation. AAAI 2007: 669-674 - 2006
- [j3]Min-Ling Zhang, Zhi-Hua Zhou:
Adapting RBF Neural Networks to Multi-Instance Learning. Neural Process. Lett. 23(1): 1-26 (2006) - [j2]Min-Ling Zhang, Zhi-Hua Zhou:
Multi-Label Neural Networks with Applications to Functional Genomics and Text Categorization. IEEE Trans. Knowl. Data Eng. 18(10): 1338-1351 (2006) - [c5]Zhi-Hua Zhou, Min-Ling Zhang:
Multi-Instance Multi-Label Learning with Application to Scene Classification. NIPS 2006: 1609-1616 - 2005
- [c4]Min-Ling Zhang, Zhi-Hua Zhou:
A k-nearest neighbor based algorithm for multi-label classification. GrC 2005: 718-721 - 2004
- [j1]Min-Ling Zhang, Zhi-Hua Zhou:
Improve Multi-Instance Neural Networks through Feature Selection. Neural Process. Lett. 19(1): 1-10 (2004) - [c3]Min-Ling Zhang, Zhi-Hua Zhou:
Ensembles of Multi-Instance Neural Networks. Intelligent Information Processing 2004: 471-474 - 2003
- [c2]Zhi-Hua Zhou, Min-Ling Zhang:
Ensembles of Multi-instance Learners. ECML 2003: 492-502 - [c1]Zhi-Hua Zhou, Min-Ling Zhang, Ke-Jia Chen:
A Novel Bag Generator for Image Database Retrieval With Multi-Instance Learning Techniques. ICTAI 2003: 565-569
Coauthor Index
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-14 21:04 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
