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2020 – today
- 2023
[j45]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)- [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) - [i14]Yi Gao, Miao Xu, Min-Ling Zhang:
Complementary to Multiple Labels: A Correlation-Aware Correction Approach. CoRR abs/2302.12987 (2023) - [i13]Zhaofei Wang, Weijia Zhang, Min-Ling Zhang:
Transformer-based Multi-Instance Learning for Weakly Supervised Object Detection. CoRR abs/2303.14999 (2023) - [i12]Hanwen Deng, Weijia Zhang, Min-Ling Zhang:
Rethinking the Value of Labels for Instance-Dependent Label Noise Learning. CoRR abs/2305.06247 (2023) - [i11]Yuheng Jia, Chongjie Si, Min-Ling Zhang:
Complementary Classifier Induced Partial Label Learning. CoRR abs/2305.09897 (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, Yu-Feng 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, Yu-Feng 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, Zhi-Jian 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, Yu-Feng 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]