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Edited Books and Monograph


  1. Kaizhu Huang, Amir Hussain, Qiufeng Wang, Rui Zhang,Deep Learning: Fundamentals, Theory, and Applications, Springer, ISBN 978-3-030-06072-5, 2019.
  2. Guoqiang Zhong and Kaizhu Huang, Semi-Supervised Learning: Background, Applications and Future Directions, Nova Science Publishers, Inc., 978-1-53613-556-5, 2018.
  3. Irwin King, Kaizhu Huang, Heike Sichtig (eds.), Part C: Machine Learning Methods, Handbook of Bio- and Neuroinformatics, Springer, 2014.
  4. Chris Brown, Heike Sichtig, Irwin King, Kaizhu Huang, Francesco Masulli (eds.), Part D: Modeling Regulatory Networks: The Systems Biology Approach, Handbook of Bio- and Neuroinformatics, Springer, 2014.
  5. Chu Kiong Loo, Keem SiahYap, KokWai Wong, Andrew Teoh, Kaizhu Huang (Eds.), Proceedings of Neural Information Processing, 21st International Conference, Part I, Lecture Notes on Computer Science 8834, Springer, 2014.
  6. Chu Kiong Loo, Keem SiahYap, KokWai Wong, Andrew Teoh, Kaizhu Huang (Eds.), Proceedings of Neural Information Processing, 21st International Conference, Part II, Lecture Notes on Computer Science 8835, Springer, 2014.
  7. Chu Kiong Loo, Keem SiahYap, KokWai Wong, Andrew Teoh, Kaizhu Huang (Eds.), Proceedings of Neural Information Processing, 21st International Conference, Part III, Lecture Notes on Computer Science 8836, Springer, 2014.
  8. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu. Machine Learning: Modeling Data Locally and Globally, Springer Verlag ISBN-13: 978-3540794516, 2008.



Representative International Journals

  1. Dong, Hang; Wang, Wei; Huang, Kaizhu; Coenen, Frans Automated Social Text Annotation with Joint Multi-Label Attention Networks, IEEE Transactions on Neural Networks and Learning Systems, accepted, 2020.
  2. Zhuang Qian, Kaizhu Huang, Qiufeng Wang, Jimin Xiao, Rui Zhang, Generative Adversarial Classifier for Handwriting Characters Super-Resolution, Pattern Recognition, 2020, accepted. (JCR Q1)
  3. Ying Ma, Guoqiang Zhong, Jinxuan Sun, Wen Liu, Kaizhu Huang, Neural CAPTCHA Networks, Applied Soft Computing, accepted, 2020. (JCR Q1)
  4. Summrina Kanwal Wajid, Amir Hussain, Kaizhu Huang, Novel Artificial Immune Networks-based Optimization of Shallow Machine Learning (ML) Classifiers, Expert Systems with Applications, accepted ,2020. (JCR Q1)
  5. Fangzhou Xiong, Zhiyong Liu, Kaizhu Huang, Xu Yang, and Amir Hussain, Encoding Primitives Generation Policy Learning for Robotic Arm to Overcome Catastrophic Forgetting in Sequential Multi-tasks Learning, Neural Networks, 2020, accepted. (JCR Q1)
  6. Hang Dong, Wei Wang, Kaizhu Huang, Frans Coenen, Knowledge Base Enrichment by Relation Learning from Social Tagging Data, Information Sciences, 526: 203-220, 2020. (JCR Q1)
  7. Zhenyu Fang, Jinchang Ren, Stephen Marshall, Huimin Zhao, Zheng Wang, Kaizhu Huang, Bing Xiao:
    Triple loss for hard face detection. Neurocomputing 398: 20-30 (2020)
  8. Kaizhu Huang, Shufei Zhang, Rui Zhang, Amir Hussain, Novel deep neural network based pattern field classification architectures, Neural Networks, accepted, 2020.
  9. Jinxuan Sun, Guoqiang Zhong, Yang Chen, Yongbin Liu, Tao Li, Kaizhu Huang, Generative Adversarial Networks with Mixture of t-Distributions Noise for Diverse Image Generation, Neural Networks, accepted, 2020
  10. Guoqiang Zhong, Wencong Jiao, Wei Gao, Kaizhu Huang, Automatic Design of Deep Networks with Neural Blocks, Cognitive Computation, accepted, 2020.
  11. Guoqiang Zhong, Yang Chen, Kaizhu Huang, Generative Adversarial Networks with Decoder-Encoder Output Noises, Neural Networks, accepted, 2020.
  12. Xinheng Wang, Muddesar Iqbal, Honghao Gao, Kaizhu Huang, Andrei Tchernykh:
    Editorial: Collaborative Computing for Data-Driven Systems. ACM Mob. Networks Appl. 25(4): 1348-1350 (2020)
  13. Fiseha B. Tesemaa, Hong Wu, Mingjian Chen, Junpeng Lin, William Zhu, Kaizhu Huang, Hybrid Channel Based Pedestrian Detection, Neurocomputing, accepted, 2020.
  14. Qiufeng Wang, Kai Yao, Rui Zhang, Amir Hussain, Kaizhu Huang, Improving DNN Performance with Kernelized Min-Max Objective, Neurocomputing, accepted, 2020.
  15. Zhenyu Fang, Jinchang Ren, Stephen Marshall, Huimin Zhao, Kaizhu Huang, Xinying Xu, Triple Loss for Hard Face Detection, Neurocomputing, accepted, 2020.
  16. Dingyuan Zheng, Jimin Xiao, Kaizhu Huang, Yao Zhao:Segmentation mask guided end-to-end person search. Signal Process. Image Commun. 86: 115876 (2020)
  17. Zhiqiang Gao, Dawei Liu, Kaizhu Huang, and Yi Huang, Context-Aware Human Activity and Smartphone Position-Mining with Motion Sensors, Remote Sensing, 11, 25-31, 2019.
  18. Xiao-Bo Jin, Guo-Sen Xie, Kaizhu Huang, Heling Cao, Qiu-Feng Wang:Discriminant Zero-Shot Learning with Center Loss. Cognitive Computation 11(4): 503-512, 2019
  19. Haochuan Jiang, Kaizhu Huang, and Rui Zhang, Amir Hussain, Style Neutralization Generative Adversarial Classifier, Cognitive Computation, (JCR Q1) 2019.
  20. Xiaobo Jin, Xu-Yao Zhang, Kaizhu Huang, Guanggang Geng, Stochastic Conjugate Gradient Algorithm with Variance Reduction, IEEE Transactions on Neural Networks and Learning Systems, 2019.
  21. Fiseha B. Tesemaa, Hong Wu, Mingjian Chen, Junpeng Lin, William Zhu, Kaizhu Huang, Hybrid Channel Based Pedestrian Detection, Neurocomputing, accepted, 2019.
  22. Qiufeng Wang, Kai Yao, Kaizhu Huang, Rui Zhang, , Amir Hussain, Improving DNN Performance with Kernelized Min-Max Objective, Neurocomputing, 2019.
  23. Yanchun Xie, Jimin Xiao, Kaizhu Huang, Jeyarajan Thiyagalingam, Yao Zhao, Correlation Filter Selection for Visual Tracking Using Reinforcement Learning, IEEE Transactions on Circuits and Systems for Video Technology, 2019.
  24. Zhi-Yong Liu, Kaizhu Huang, Xu Yang, Cheng-Lin Liu, Introduction to the Special Issue of Advances in Graph Algorithm and Applications, Neurocomputing, 336:1-2, 2019. (JCR Q1, 2016 ISI impact factor 3.337) (JCR Q1)
  25. Jieming Ma, Haochuan Jiang, Ziqiang Bi, Kaizhu Huang et al. , Maximum Power Point Estimation for Photovoltaic Strings Subjected to Partial Shading Scenarios, IEEE Transactions on Industry Applications, accepted,2018.
  26. Xu Yang, Lu Zhang, Zhiyong Liu, Shifeng Zhang, Kaizhu Huang, Amir Hussain, Hong Qiao Cross-Modality Interactive Attention Network for Multispectral Pedestrian Detection, Information Fusion, accepted, 2018. (JCR Q1) (CAS JCR Q1)
  27. Jianyu Sun, Guoqiang Zhong, Kaizhu Huang, Junyu Dong, Banzhaf Random Forests: Cooperative Game Theory Based Random Forests with Consistency, Neural Networks, accepted, 2018.
  28. Xiaobo Jin, Guosen Xie, Kaizhu Huang, Amir Hussain', Cognitive Computation, Accelerating Infinite Ensemble of Clustering by Pivot Features, accepted, 2018. (JCR Q1).
  29. Xi Yang, Kaizhu Huang, Rui Zhang, Yannis Goulermas, A Novel Deep Density Model for Unsupervised Learning, Cognitive Computation, accepted, 2018. (JCR Q1).
  30. Xi Yang, Kaizhu Huang, Rui Zhang, Amir Hussain, Yannis Goulermas, A New Two-layer Mixture of Factor Analyzers with Joint Factor Loading Model for the Classification of Small Dataset Problems, Neurocomputing, accepted, 2018. (JCR Q1).
  31. Jimin Xiao, Yanchun Xie, Tammam Tillo, Kaizhu Huang, Yunchao Wei, Jiashi Feng: IAN: The Individual Aggregation Network for Person Search, Pattern Recognition, accepted, 2018.
  32. Yanchun Xie, Jimin Xiao, Kaizhu Huang, Jeyarajan Thiyagalingam, Yao Zhao, Correlation Filter Selection for Visual Tracking Using Reinforcement Learning, IEEE Transactions on Circuits and Systems for Video Technology, accepted, 2018. (CAS JCR Q2)
  33. Fanzhou Xiong, Biao Sun, Xu Yang,Kaizhu Huang, Hong Qiao, Amir Hussain, Zhi-Yong Liu,Guided Policy Search for Sequential Multi-Task Learning, IEEE Transactions on Systems Man and Cybernetics-Systems, Pages 1-11, issues 99,2018.
  34. Summrina Kanwal Wajid, Amir Hussain, Kaizhu Huang, Three-Dimensional Local Energy-Based Shape Histogram (3D-LESH)-Based Feature Extraction‒ A Novel Technique, Expert Systems With Applications, Pages 388-400, Vol. 112, 2018. (JCR Q1)
  35. Xi Yang, Kaizhu Huang, Rui Zhang, Amir Hussain, Learning Latent Features with Infinite Non-negative Binary Matrix Tri-factorization, IEEE Transactions on Emerging Topics in Computational Intelligence, accepted, 2018.
  36. Xiao-Bo Jin, Guang-Gang Geng, Guo-Sen Xie, Kaizhu Huang, Pair-wise Loss for Optimizing NDCG Approximately, Information Sciences, Volume 453, Pages 50-65, 2018. (JCR Q1)
  37. Changzhi Luo, Meng Wang, Kaizhu Huang, Jiashi Feng, Zero-Shot Learning via Attribute Regression and Class Prototype Rectification, IEEE Transactions on Image Processing, 27(2):637-648, 2018. (JCR Q1)
  38. Peipei Yang, Kaizhu Huang, Amir Hussain, A Review on Multi-task Metric Learning, BMC Big Data Analytics, accepted, 2018.
  39. Chenru Jiang, Jimin Xiao, Tammam Tillo, Kaizhu Huang, Siamese Network Ensemble for Visual Tracking, Neurocomputing,275: 2892-2903, 2018. (JCR Q1)
  40. Kaizhu Huang, Haochuan Jiang, Xu-Yao Zhang, Rui Zhang, Field Support Vector Machines, IEEE Transactions on Emerging Topics in Computational Intelligence, 1(6), 454-463, 2017.
  41. Jieming Ma, Haochuan Jiang, Kaizhu Huang, Ziqiang Bi, Kalok Man, Novel Field-Support Vector Regression-Based Soft Sensor for Accurate Estimation of Solar Irradiance, IEEE Transactions on Circuits and Systems I, 64(12): 3183-3191, 2017. (JCR Q2)
  42. Kaizhu Huang, Rui Zhang, Xiao-Bo Jin, Amir Hussain, Special Issue Editorial: Cognitively Inspired Computing for Knowledge Discovery, Cognitive Computation, to appear, 2017. (JCR Q1)
  43. Guoqiang Zhong, Shoujun Yan, Kaizhu Huang, Junyu Dong, Reducing and Stretching Deep Convolutional Activation Features for Accurate Image Classification, Cognitive Computation, accepted, 2017. (JCR Q1)
  44. Shufei Zhang, Kaizhu Huang, Rui Zhang, Amir Hussain, Learning from Few Samples with Memory Network, Cognitive Computation, accepted, 2018. (JCR Q1)
  45. Qiufeng Wang, Kaizhu Huang, Song Li, and Wei Yu, Adaptive Modeling for Large-scale Advertisers Optimization, BMC Big Data Analytics, 2-8, 2017.
  46. Xi Yang, Kaizhu Huang, Y.J. Yannis, Rui Zhang, Joint Learning of Unsupervised Dimensionality Reduction and Gaussian Mixture Model, Neural Processing Letters, Volume 45, Issue 3, pp 791–806, 2017. (2014 ISI Impact Factor 1.448) (JCR Q3)
  47. Adnan Amin, Sajid Anwar, Awais Adnan, Muhammad Nawaz, Khalid Alawfi, Amir Hussain, Kaizhu Huang ,Customer churn prediction in the telecommunication sector using a rough set approach. Neurocomputing, 237: 242-254, 2017 (JCR Q1)
  48. Yao Lu, Kaizhu Huang, Cheng-Lin Liu, Doubly Stochastic Projected Fixed-Point Algorithm for Large Graph Matching, Pattern Recognition, Vol. 60, 971-982, 2016. (2014 ISI Impact Factor 3.096) (JCR Q1)
  49. Haoda Chu, Kaizhu Huang, Rui Zhang, Amir Hussain, SDRNF: Generating Scalable and Discriminative Random Nonlinear Features from Data, BMC Big Data Analytics, 1:10, 2016.
  50. Shi Cheng, Bin Liu, T. O. Ting, Quande Qin,Yuhui Shi,Kaizhu Huang, Survey on Data Science with Population-based Algorithms, BMC Big Data Analytics, 1:3, 2016.
  51. Haochuan Jiang, Kaizhu Huang, Tingting Mu, Rui Zhang, Cheng Wang, Robust One-shot Facial Expression Recognition with Eye-glasses, International Journal of Machine Learning and Computing (IJMLC),Vol. 6 No.2:80-86, 2016.
  52. Tiew On Ting, Jieming Ma, Kyeong Soo Kim, and Kaizhu Huang, Multicores and GPU Utilization in Parallel Swarm Algorithm for Parameter Estimation of Photovoltaic Model, Applied Soft Computing,Vol 40, Pages 58-63, 2016. (JCR Q1)
  53. Haiqin Yang, Kaizhu Huang, Irwin King, Michael R. Lyu, Maximum Margin Semi-supervised Learning with Irrelevant Data, Neural Networks, 70: 90-102, 2015. (JCR Q1)
  54. Yan-Ming Zhang, Kaizhu Huang, Cheng-Lin Liu, MTC: A Fast and Robust Graph-based Transductive Learning Algorithm, IEEE Transactions on Learning Systems and Neural Networks, 26(9): 1979-1991, 2015. (JCR Q1)
  55. Kaizhu Huang, Rui Zhang, Xu-Cheng Yin, Imbalance Learning locally and Globally, Neural Processing Letters, 41(3): 311-323, 2015. (JCR Q3)
  56. Xu-Cheng Yin, Kaizhu Huang, Hong-Wei Hao: DE2: Dynamic ensemble of ensembles for learning nonstationary data, Neurocomputing, 165: 14-22, 2015. (JCR Q1)
  57. Xu-Yao Zhang, Peipei Yang, Yan-Ming Zhang, Kaizhu Huang, and Cheng-Lin Liu, Combination of Classification and Clustering Results with Label Propagation, IEEE Signal Processing Letters, 21(5): 610-614, 2014. (JCR Q2)
  58. Yan-Ming Zhang, Kaizhu Huang, Xinwen Hou, and Cheng-Lin Liu, Learning Locality Preserving Graph from Data, IEEE Trans. Cybnetics, 44(11): 2088-2098, 2014. (JCR Q1)
  59. Xu-Cheng Yin, Kuang Yin, Kaizhu Huang, Hong-Wei Hao, Robust Text Detection in Natural Images, IEEE Trans. on Pattern Analysis and Machine Intelligence, 36(5): 970-983, 2014. (JCR Q1)
  60. Xu-Cheng Yin, Kaizhu Huang, Hong-Wei Hao, A novel heuristic classifier ensemble method with sparsity and diversity, Neurocomputing, in press, available online 27 January 2014. (JCR Q1)
  61. Bo Xu, Kaizhu Huang, Irwin King, Cheng-lin Liu, Jun Sun, Satoshi Naoi, Graphical Lasso Quadratic Discriminant Function and Its Application to Character Recognition, Neurocomputing, 129: 33-40, 2014. (JCR Q1)
  62. Xu-Cheng Yin, Kaizhu Huang, Hong-Wei Hao, Convex Ensemble Learning with Sparsity and Diversity, Information Fusion Journal, in press, 2014. (JCR Q1)
  63. Peipei Yang, Kaizhu Huang, Cheng-lin Liu, Geometry Preserving Multi-task Metric Learning, Machine Learning, Volume 92(1), 133-175, 2013. (JCR Q2)
  64. Peipei Yang, Kaizhu Huang, Cheng-lin Liu, A Multi-task Framework for Metric Learning with Common Subspace, Neural Computing and Applications, Volume 22 (7-8), 1337-1347, 2013. (JCR Q2)
  65. Guoqiang Zhong, Kaizhu Huang, Cheng-Lin Liu: Joint learning of error-correcting output codes and dichotomizers from data. Neural Computing and Applications 21(4): 715-724, 2012. (JCR Q2)
  66. Bo Xu, Kaizhu Huang, Cheng-Lin Liu: Maxi-Min Discriminant Analysis via Online Learning. Neural Networks 34: 56-64, 2012. (JCR Q1)
  67. Kaizhu Huang, Yiming Ying, Colin Campbell, Generalized Sparse Metric Learning With Relative Comparisons. Knowledge and Information Systems (KAIS), Volume 28, Issue 1, pages 25-45, 2011. (JCR Q2)
  68. Zhi-Bin Wang, Hong-Wei Hao, Xu-Cheng Yin, Qian Liu, Kaizhu Huang: Exchange rate prediction with non-numerical information. Neural Computing and Applications 20(7): 945-954, 2011. (JCR Q2)
  69. Xu-Cheng Yin, Qian Liu, Hong-Wei Hao, Zhi-Bin Wang, Kaizhu Huang: FMI image based rock structure classification using classifier combination. Neural Computing and Applications 20(7): 955-963, 2011. (JCR Q2)
  70. Kaizhu Huang, Danian Zheng, Jun Sun, Yoshinobu Hotta, Katsuhito Fujimoto, Satoshi Naoi: Sparse learning for support vector classification. Pattern Recognition Letters 31(13): 1944-1951, 2010. (JCR Q2)
  71. Kaizhu Huang, Danian Zheng, Irwin King, Michael R. Lyu, Arbitrary Norm Support Vector Machines. Neural Computation, Vol. 21, No. 2: 560–582, 2009. (JCR Q2)
  72. Haiqin Yang, Kaizhu Huang, Irwin King, Michael R. Lyu. Local Support Vector Regression for Time Series Prediction. Neurocomputing, Volume 72, Issues 10-12, Pages 2659-2669, 2009. (JCR Q1)
  73. Yiming Ying, Kaizhu Huang, Colin Campbell, Enhanced Protein Fold Recognition through a Novel Data Integration Approach. BMC Bioinformatics, Vol.10:267, 2009. (JCR Q1)
  74. Zenglin Xu, Kaizhu Huang, Jianke Zhu, Irwin King, Michael R. Lyu: A novel kernel-based maximum a posteriori classification method. Neural Networks 22(7): 977-987, 2009. (JCR Q1)
  75. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu. M4: Learning Large Margin Machines Locally and Globally, IEEE Trans. Neural Networks, vol. 19, iss. 2, pp. 260-272, 2008. (JCR Q1)
  76. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Maximizing Sensitivity in Medical Diagnosis Using Biased Minimax Probability Machine. IEEE Trans. Biomedical Engineering, Vol 53, Issue 5, 821- 831, May 2006. (JCR Q1)
  77. Kaizhu Huang, Haiqin Yang, Irwin King, and Michael R. Lyu, Imbalanced Learning With Biased Minimax Probability Machine. IEEE Trans. System Man, Cybnetics, Part B, Vol 36, No 4, 913 – 923, August, 2006. (JCR Q1)
  78. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, and Laiwan Chan, The Minimum Error Minimax Probability Machine. Journal of Machine Learning Research, Vol. 5, pp. 1253-1286, October 2004. (JCR Q1)


Representative Top Conferences (23 over 90+)

  1. Pay Attention Selectively and Comprehensively: Pyramid Gating Network for Human Pose Estimation without Pretraining, Chenru Jiang, Kaizhu Huang, Shufei Zhang, Henry Wang and Jimin Xiao, ACM Multimedia (ACM MM), 2020.(acceptance rate 27.8%).
  2. Inductive Generalized Zero-shot Learning with Adversarial Relation Network, Guanyu Yang, Kaizhu Huang, Rui Zhang, John Goulermas and Amir Hussain, European Conference on Machine Learning (ECML), 2020.
  3. Matching Representations Matters: End-to-End Learning for Neural Texture Transfer, Yanchun Xie, Jimin XIAO, Mingjie Sun, Chao Yao, Kaizhu Huang, European Conference on Computer Vision (ECCV), 2020.(spotlight paper, acceptance rate 5%).
  4. Towards Better Forecasting by Fusing Near and Distant Future Visions, Jiezhu Cheng, Kaizhu Huang, Zibin Zheng, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
  5. Bingfeng Zhang, Jimin Xiao, Yunchao Wei, Mingjie Sun, Kaizhu Huang, Reliability Does Matter: An End-to-End Weakly Supervised Semantic Segmentation Approach, Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), 2020.
  6. Xiao-Bo Jin, Guo-Sen Xie, Kaizhu Huang, Jianyu Miao, Qiufeng Wang, Beyond Attributes: High-order Attribute Features for Zero-shot Learning, In International Conference on Computer Vision Workshop (ICCV-W), 2019.
  7. Shufei Zhang, Kaizhu Huang, Rui Zhang, and Amir Hussain, Generalized Adversarial Training in Riemannian Space , In IEEE Fifteen Conference on Data Mining (ICDM’2019) , 2019, (acceptance rate 9.1%).
  8. Xi Yang, Yuyao Yan, Kaizhu Huang, and Rui Zhang, VSB-DVM: An End-to-end Bayesian Nonparametric Generalization of Deep Variational Mixture Model, In IEEE Fifteen Conference on Data Mining (ICDM’2019) , 2019, (acceptance rate 9.1%).
  9. Hang Dong, Wei Wang, Kaizhu Huang and Frans Coenen, Joint Multi-Label Attention Networks for Social Text Annotation, In Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2019)" , 2019, (acceptance rate 22.6%) .
  10. Chunchun Lv, Kaizhu Huang, and Hai-Ning Liang, A Unified Gradient Regularization Family for Adversarial Examples" In IEEE Fifteen Conference on Data Mining (ICDM’2015) , 2015, (acceptance rate 8.4%).
  11. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Scalable Data Analytics: Theory and Applications, in Eighth ACM International Conference on Web Search and Data Mining (WSDM’2015), 2015.
  12. Xu-Yao Zhang, Kaizhu Huang, and Chenglin Liu, Feature Transformation with Class Conditional Decorrelation. In IEEE Thirteen Conference on Data Mining (ICDM’2013), pages 887-896, 2013. (Acceptance Rate = 94/809 =11.6%)
  13. Yan-Ming Zhang, Kaizhu Huang, Guanggang Geng, Cheng-Lin Liu, Fast kNN Graph Construction with Locality Sensitive Hashing, In European conference on Machine Learning (ECML’2013), (Acceptance Rate =25.0%)
  14. Xu-Cheng Yin, Xuwang Yin, Kaizhu Huang, Hong-Wei Hao, Accurate and Robust Text Detection: A Step-In for Text Retrieval in Natural Scene Images, ACM Special Interest Group on Information Retrieval (SIGIR’2013), pages 1091-1092, 2013.
  15. Peipei Yang, Kaizhu Huang, and Cheng-Lin Liu, Geometry Preserving Multi-task Metric Learning. In European conference on Machine Learning (ECML’2012), LNCS Vol.7523, pp.648-664, 2012 (Acceptance Rate =23.7%)
  16. Guoqiang Zhong, Kaizhu Huang, and Cheng-Lin Liu, Low Rank Metric Learning with Manifold Regularization. In IEEE Eleventh conference on Data Mining (ICDM’2011), 1266-1271, 2011. (Short paper, Acceptance Rate =18%)
  17. Yan-Ming Zhang, Kaizhu Huang, and Cheng-Lin Liu,Fast and Robust Graph-based Transductive Learning via Minimum Tree Cut, IEEE Eleventh conference on Data Mining (ICDM’2011), 952-961, 2011. (Full paper, Acceptance Rate =12%)
  18. Xu-Yao Zhang, Kaizhu Huang, and Cheng-Lin Liu, Pattern Field Classification with Style Normalized Transformation, In The International Joint Conference on Artificial Intelligence (IJCAI’2011), 1621-1626, 2011. (Acceptance Rate =227/1325=17%)
  19. Kaizhu Huang, Rong Jin, Zenglin Xu, and Cheng-Lin Liu Robust Metric Learning with Smooth Optimization , In The 26th Conference on Uncertainty in Artificial Intelligence (UAI’2010), 244-251,2010. (Acceptance Rate =30/260=11.3%)
  20. Kaizhu Huang, Yiming Ying, Colin Campbell, GSML: A Unified Framework for Sparse Metric Learning,IEEE Ninth conference on Data Mining (ICDM’2009), 189-198, 2009. (Acceptance Rate = 70/786 = 8.9%)
  21. Yiming Ying, Kaizhu Huang, Colin Campbell,, Sparse Metric Learning via Smooth Optimization, in Proc. Advances in Neural Information Processing System 22 (NIPS’2009), Cambridge, MA, 2009. (Acceptance Rate = 263/1105 = 23.8%)
  22. Zenglin Xu, Rong Jin, Kaizhu Huang, Irwin King, Michael R. Lyu, Semi-supervised Text Categorization by Active Search, In ACM 17th Conference on Information and Knowledge Management (CIKM’2008), 1517-1518, 2008. (Acceptance rate = 132+124)/772=16%)
  23. Kaizhu Huang, Zenglin Xu, Irwin King, Michael R. Lyu, Semi-supervised Learning from General Unlabeled Data, In IEEE Eighth conference on Data Mining (ICDM’2008), 273-282, 2008. (Acceptance Rate =70/724=9.6%)
  24. Kaizhu Huang, Irwin King, Michael R. Lyu, Direct Zero-norm Optimization for Feature Selection, In IEEE Eighth Conference on Data Mining (ICDM’2008), 845-850, 2008. Short paper (Acceptance Rate=(70+74)/724 =20%)
  25. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Learning Classifiers from Imbalanced Data Based on Biased Minimax Probability Machine. Proceedings IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’2004), 558-563, Washington, DC, June 27 -July 2, 2004. (Acceptance Rate = 17%)
  26. Kaizhu Huang, Haiqin Yang, Irwin King, Michael R. Lyu, Learning Large Margin Machines Locally and Globally. Proceedings international Conference on Machine Learning (ICML’2004)'', Banff, Canada, pp. 401-408, 2004. (Acceptance Rate = 32%) 
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