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2020 – today
- 2022
[j53]Siwei Lyu
, Yanbo Fan, Yiming Ying, Bao-Gang Hu:
Average Top-k Aggregate Loss for Supervised Learning. IEEE Trans. Pattern Anal. Mach. Intell. 44(1): 76-86 (2022)- [j52]Yifan Xu, Kekai Sheng, Weiming Dong, Baoyuan Wu, Changsheng Xu, Bao-Gang Hu:
Towards Corruption-Agnostic Robust Domain Adaptation. ACM Trans. Multim. Comput. Commun. Appl. 18(4): 99:1-99:16 (2022) - 2021
- [j51]Kekai Sheng, Weiming Dong, Haibin Huang, Menglei Chai, Yong Zhang, Chongyang Ma, Bao-Gang Hu:
Learning to assess visual aesthetics of food images. Comput. Vis. Media 7(1): 139-152 (2021) - [j50]Huai-Yu Li, Weiming Dong, Bao-Gang Hu:
Incremental Concept Learning via Online Generative Memory Recall. IEEE Trans. Neural Networks Learn. Syst. 32(7): 3206-3216 (2021) - [i21]Bao-Gang Hu, Wei-Ming Dong:
A design of human-like robust AI machines in object identification. CoRR abs/2101.02327 (2021) - [i20]Yifan Xu, Kekai Sheng, Weiming Dong, Baoyuan Wu, Changsheng Xu, Bao-Gang Hu:
Towards Corruption-Agnostic Robust Domain Adaptation. CoRR abs/2104.10376 (2021) - 2020
- [j49]Yanbo Fan, Baoyuan Wu, Ran He, Bao-Gang Hu, Yong Zhang, Siwei Lyu:
Groupwise Ranking Loss for Multi-Label Learning. IEEE Access 8: 21717-21727 (2020) - [c71]Kekai Sheng, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma:
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning. AAAI 2020: 5709-5716 - [i19]Bao-Gang Hu, Han-Bing Qu:
Generalized Constraints as A New Mathematical Problem in Artificial Intelligence: A Review and Perspective. CoRR abs/2011.06156 (2020)
2010 – 2019
- 2019
- [j48]Yong Zhang, Yanbo Fan, Weiming Dong, Bao-Gang Hu, Qiang Ji:
Semi-Supervised Deep Neural Network for Joint Intensity Estimation of Multiple Facial Action Units. IEEE Access 7: 150743-150756 (2019) - [j47]Thomas Corpetti, Xing Gong, Meng-Zhen Kang, Bao-Gang Hu, Laurence Hubert-Moy:
Time-consistent estimation of LAI by assimilation in GreenLab plant growth model. Comput. Geosci. 130: 57-68 (2019) - [c70]Yong Zhang, Baoyuan Wu, Weiming Dong, Zhifeng Li, Wei Liu, Bao-Gang Hu, Qiang Ji:
Joint Representation and Estimator Learning for Facial Action Unit Intensity Estimation. CVPR 2019: 3457-3466 - [c69]Huai-Yu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu:
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning. ICML 2019: 3825-3834 - [i18]Bao-Gang Hu, Weiming Dong:
"Ge Shu Zhi Zhi": Towards Deep Understanding about Worlds. CoRR abs/1901.01834 (2019) - [i17]Huai-Yu Li, Weiming Dong, Xing Mei, Chongyang Ma, Feiyue Huang, Bao-Gang Hu:
LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning. CoRR abs/1905.06331 (2019) - [i16]Huai-Yu Li, Weiming Dong, Bao-Gang Hu:
Incremental Concept Learning via Online Generative Memory Recall. CoRR abs/1907.02788 (2019) - [i15]Kekai Sheng, Weiming Dong, Menglei Chai, Guohui Wang, Peng Zhou, Feiyue Huang, Bao-Gang Hu, Rongrong Ji, Chongyang Ma:
Revisiting Image Aesthetic Assessment via Self-Supervised Feature Learning. CoRR abs/1911.11419 (2019) - 2018
- [j46]Xing-Rong Fan, Xiujuan Wang, Meng-Zhen Kang, Jing Hua, Shuangsheng Guo, Philippe de Reffye, Bao-Gang Hu:
A knowledge-and-data-driven modeling approach for simulating plant growth and the dynamics of CO2/O2 concentrations in a closed system of plants and humans by integrating mechanistic and empirical models. Comput. Electron. Agric. 148: 280-290 (2018) - [j45]Huai-Yu Li, Wei-Ming Dong, Bao-Gang Hu:
Facial Image Attributes Transformation via Conditional Recycle Generative Adversarial Networks. J. Comput. Sci. Technol. 33(3): 511-521 (2018) - [j44]Zhi-Yong Ran, Wei Wang, Bao-Gang Hu:
On connections between Rényi entropy Principal Component Analysis, kernel learning and graph embedding. Pattern Recognit. Lett. 112: 125-130 (2018) - [j43]Guibiao Xu, Bao-Gang Hu, José C. Príncipe:
Robust C-Loss Kernel Classifiers. IEEE Trans. Neural Networks Learn. Syst. 29(3): 510-522 (2018) - [c68]Yong Zhang, Weiming Dong, Bao-Gang Hu, Qiang Ji:
Weakly-Supervised Deep Convolutional Neural Network Learning for Facial Action Unit Intensity Estimation. CVPR 2018: 2314-2323 - [c67]Yong Zhang, Weiming Dong, Bao-Gang Hu, Qiang Ji:
Classifier Learning With Prior Probabilities for Facial Action Unit Recognition. CVPR 2018: 5108-5116 - [c66]Yong Zhang, Rui Zhao, Weiming Dong, Bao-Gang Hu, Qiang Ji:
Bilateral Ordinal Relevance Multi-Instance Regression for Facial Action Unit Intensity Estimation. CVPR 2018: 7034-7043 - [c65]Kekai Sheng, Weiming Dong, Chongyang Ma, Xing Mei, Feiyue Huang, Bao-Gang Hu:
Attention-based Multi-Patch Aggregation for Image Aesthetic Assessment. ACM Multimedia 2018: 879-886 - [c64]Kekai Sheng, Weiming Dong, Haibin Huang, Chongyang Ma, Bao-Gang Hu:
Gourmet photography dataset for aesthetic assessment of food images. SIGGRAPH Asia Technical Briefs 2018: 20:1-20:4 - 2017
- [j42]Zhi-Yong Ran, Bao-Gang Hu:
Parameter Identifiability in Statistical Machine Learning: A Review. Neural Comput. 29(5): 1151-1203 (2017) - [j41]Guibiao Xu, Zheng Cao, Bao-Gang Hu, José C. Príncipe:
Robust support vector machines based on the rescaled hinge loss function. Pattern Recognit. 63: 139-148 (2017) - [j40]Baoyuan Wu, Bao-Gang Hu, Qiang Ji:
A Coupled Hidden Markov Random Field model for simultaneous face clustering and tracking in videos. Pattern Recognit. 64: 361-373 (2017) - [j39]Kekai Sheng, Weiming Dong, Wei Li, Joseph Razik, Feiyue Huang, Bao-Gang Hu:
Centroid-aware local discriminative metric learning in speaker verification. Pattern Recognit. 72: 176-185 (2017) - [j38]Yong Zhang, Weiming Dong, Chongyang Ma, Xing Mei, Ke Li, Feiyue Huang, Bao-Gang Hu, Oliver Deussen:
Data-Driven Synthesis of Cartoon Faces Using Different Styles. IEEE Trans. Image Process. 26(1): 464-478 (2017) - [c63]Yanbo Fan, Ran He, Jian Liang, Bao-Gang Hu:
Self-Paced Learning: An Implicit Regularization Perspective. AAAI 2017: 1877-1883 - [c62]Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu:
Learning with Average Top-k Loss. NIPS 2017: 497-505 - [c61]Yingying Deng, Fan Tang, Weiming Dong, Hanxing Yao, Bao-Gang Hu:
Style-oriented representative paintings selection. SIGGRAPH ASIA (Posters) 2017: 12:1-12:2 - [i14]Yanbo Fan, Jian Liang, Ran He, Bao-Gang Hu, Siwei Lyu:
Robust Localized Multi-view Subspace Clustering. CoRR abs/1705.07777 (2017) - [i13]Yanbo Fan, Siwei Lyu, Yiming Ying, Bao-Gang Hu:
Learning with Average Top-k Loss. CoRR abs/1705.08826 (2017) - 2016
- [j37]Bao-Gang Hu, Hong-Jie Xing:
An Optimization Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications. Entropy 18(2): 59 (2016) - [j36]Zhi-Yong Ran, Bao-Gang Hu:
Reply to "Reply to 'Determining structural identifiability of parameter learning machines'". Neurocomputing 218: 318-319 (2016) - [c60]Chun-Guo Li, Xing Mei, Bao-Gang Hu:
Unsupervised ranking of multi-attribute objects based on principal curves. ICDE 2016: 1526-1527 - [c59]Guibiao Xu, Bao-Gang Hu, José C. Príncipe:
Robust bounded logistic regression in the class imbalance problem. IJCNN 2016: 1434-1441 - [c58]Linlin Cao, Ran He, Bao-Gang Hu:
Locally imposing function for Generalized Constraint Neural Networks - A study on equality constraints. IJCNN 2016: 4795-4802 - [i12]Linlin Cao, Ran He, Bao-Gang Hu:
Locally Imposing Function for Generalized Constraint Neural Networks - A Study on Equality Constraints. CoRR abs/1604.05198 (2016) - [i11]Yanbo Fan, Ran He, Jian Liang, Bao-Gang Hu:
Self-Paced Learning: an Implicit Regularization Perspective. CoRR abs/1606.00128 (2016) - 2015
- [j35]Kekai Sheng, Weiming Dong, Yan Kong, Xing Mei, Ji-Lin Li, Chengjie Wang, Feiyue Huang, Bao-Gang Hu:
Evaluating the Quality of Face Alignment without Ground Truth. Comput. Graph. Forum 34(7): 213-223 (2015) - [j34]Zhi-Yong Ran, Bao-Gang Hu:
An identifying function approach for determining parameter structure of statistical learning machines. Neurocomputing 162: 209-217 (2015) - [j33]Baoyuan Wu, Siwei Lyu, Bao-Gang Hu, Qiang Ji:
Multi-label learning with missing labels for image annotation and facial action unit recognition. Pattern Recognit. 48(7): 2279-2289 (2015) - [j32]Chun-Guo Li, Xing Mei, Bao-Gang Hu:
Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves. IEEE Trans. Knowl. Data Eng. 27(12): 3404-3416 (2015) - [c57]Yanbo Fan, Ran He, Bao-Gang Hu:
Global and local consistent multi-view subspace clustering. ACPR 2015: 564-568 - [c56]Xing Mei, Weiming Dong, Bao-Gang Hu, Siwei Lyu:
UniHIST: A unified framework for image restoration with marginal histogram constraints. CVPR 2015: 3753-3761 - [c55]Xing Mei, Honggang Qi, Bao-Gang Hu, Siwei Lyu:
Improving Image Restoration with Soft-Rounding. ICCV 2015: 459-467 - [c54]Linlin Cao, Bao-Gang Hu:
Generalized constraint neural network regression model subject to equality function constraints. IJCNN 2015: 1-8 - [i10]Bao-Gang Hu:
Information Theory and its Relation to Machine Learning. CoRR abs/1501.04309 (2015) - [i9]Xing Mei, Honggang Qi, Bao-Gang Hu, Siwei Lyu:
Improving Image Restoration with Soft-Rounding. CoRR abs/1508.05046 (2015) - 2014
- [b1]Ran He, Bao-Gang Hu, Xiaotong Yuan, Liang Wang:
Robust Recognition via Information Theoretic Learning. Springer Briefs in Computer Science, Springer 2014, ISBN 978-3-319-07415-3, pp. 1-102 - [j31]Zhi-Yong Ran, Bao-Gang Hu:
Determining structural identifiability of parameter learning machines. Neurocomputing 127: 88-97 (2014) - [j30]Wei Wang, Bao-Gang Hu, Zengfu Wang:
Globality and locality incorporation in distance metric learning. Neurocomputing 129: 185-198 (2014) - [j29]Zhi-Yong Ran, Bao-Gang Hu:
Determining parameter identifiability from the optimization theory framework: A Kullback-Leibler divergence approach. Neurocomputing 142: 307-317 (2014) - [j28]Xiaowan Zhang, Bao-Gang Hu:
A New Strategy of Cost-Free Learning in the Class Imbalance Problem. IEEE Trans. Knowl. Data Eng. 26(12): 2872-2885 (2014) - [j27]Bao-Gang Hu:
What Are the Differences Between Bayesian Classifiers and Mutual-Information Classifiers? IEEE Trans. Neural Networks Learn. Syst. 25(2): 249-264 (2014) - [c53]Chun-Guo Li, Xing Mei, Bao-Gang Hu:
Two-Phase Attribute Ordering for Unsupervised Ranking of Multi-attribute Objects. ICDM Workshops 2014: 175-182 - [c52]Xing Mei, Bao-Gang Hu, Siwei Lyu:
Non-blind image restoration with symmetric generalized Pareto priors. ICIP 2014: 4477-4481 - [c51]Baoyuan Wu, Zhilei Liu, Shangfei Wang, Bao-Gang Hu, Qiang Ji:
Multi-label Learning with Missing Labels. ICPR 2014: 1964-1968 - [c50]Guibiao Xu, Bao-Gang Hu, José C. Príncipe:
An asymmetric stagewise least square loss function for imbalanced classification. IJCNN 2014: 1107-1114 - [c49]Zhi-Yong Ran, Bao-Gang Hu:
An identifying function approach for determining structural identifiability of parameter learning machines. IJCNN 2014: 1593-1599 - [c48]Yong Zhang, Weiming Dong, Oliver Deussen, Feiyue Huang, Ke Li, Bao-Gang Hu:
Data-driven face cartoon stylization. SIGGRAPH ASIA Technical Briefs 2014: 14:1-14:4 - [i8]Chun-Guo Li, Xing Mei, Bao-Gang Hu:
Unsupervised Ranking of Multi-Attribute Objects Based on Principal Curves. CoRR abs/1402.4542 (2014) - [i7]Bao-Gang Hu, Weiming Dong:
A study on cost behaviors of binary classification measures in class-imbalanced problems. CoRR abs/1403.7100 (2014) - 2013
- [j26]Ran He, Wei-Shi Zheng, Bao-Gang Hu, Xiangwei Kong:
Two-Stage Nonnegative Sparse Representation for Large-Scale Face Recognition. IEEE Trans. Neural Networks Learn. Syst. 24(1): 35-46 (2013) - [c47]Baoyuan Wu, Yifan Zhang, Bao-Gang Hu, Qiang Ji:
Constrained Clustering and Its Application to Face Clustering in Videos. CVPR 2013: 3507-3514 - [c46]Baoyuan Wu, Siwei Lyu, Bao-Gang Hu, Qiang Ji:
Simultaneous Clustering and Tracklet Linking for Multi-face Tracking in Videos. ICCV 2013: 2856-2863 - [c45]Guibiao Xu, Bao-Gang Hu:
Cost-Free Learning for Support Vector Machines with a Reject Option. ICDM Workshops 2013: 817-824 - [c44]Wei Wang, Bao-Gang Hu, Zengfu Wang:
Efficient and Scalable Information Geometry Metric Learning. ICDM 2013: 1217-1222 - [c43]Chun-Guo Li, Bao-Gang Hu:
Robust principal curves based on maximum correntropy criterion. ICMLC 2013: 615-620 - [i6]Bao-Gang Hu, Hong-Jie Xing:
A New Approach of Deriving Bounds between Entropy and Error from Joint Distribution: Case Study for Binary Classifications. CoRR abs/1303.0943 (2013) - [i5]Xiaowan Zhang, Bao-Gang Hu:
A New Strategy of Cost-Free Learning in the Class Imbalance Problem. CoRR abs/1307.5730 (2013) - 2012
- [j25]Ning Wang, Bao-Gang Hu:
Real-Time Simulation of Aeolian Sand Movement and Sand Ripple Evolution: A Method Based on the Physics of Blown Sand. J. Comput. Sci. Technol. 27(1): 135-146 (2012) - [j24]Lin Wu, François-Xavier Le Dimet, Philippe de Reffye, Bao-Gang Hu, Paul-Henry Cournède, Meng-Zhen Kang:
An optimal control methodology for plant growth - Case study of a water supply problem of sunflower. Math. Comput. Simul. 82(5): 909-923 (2012) - [j23]Xiao-Tong Yuan, Bao-Gang Hu, Ran He:
Agglomerative Mean-Shift Clustering. IEEE Trans. Knowl. Data Eng. 24(2): 209-219 (2012) - [j22]Shuang-Hong Yang, Bao-Gang Hu:
Discriminative Feature Selection by Nonparametric Bayes Error Minimization. IEEE Trans. Knowl. Data Eng. 24(8): 1422-1434 (2012) - [c42]Xiaowan Zhang, Bao-Gang Hu:
Learning in the Class Imbalance Problem When Costs are Unknown for Errors and Rejects. ICDM Workshops 2012: 194-201 - [c41]Wei Wang, Bao-Gang Hu, Zengfu Wang:
Discriminating classes collapsing for Globality and Locality Preserving Projections. IJCNN 2012: 1-8 - [i4]Bao-Gang Hu, Hong-Jie Xing:
Analytical Bounds between Entropy and Error Probability in Binary Classifications. CoRR abs/1205.6602 (2012) - 2011
- [j21]Ran He, Wei-Shi Zheng, Bao-Gang Hu, Xiangwei Kong:
A Regularized Correntropy Framework for Robust Pattern Recognition. Neural Comput. 23(8): 2074-2100 (2011) - [j20]Ran He, Wei-Shi Zheng, Bao-Gang Hu:
Maximum Correntropy Criterion for Robust Face Recognition. IEEE Trans. Pattern Anal. Mach. Intell. 33(8): 1561-1576 (2011) - [j19]Ran He, Bao-Gang Hu, Wei-Shi Zheng, Xiangwei Kong:
Robust Principal Component Analysis Based on Maximum Correntropy Criterion. IEEE Trans. Image Process. 20(6): 1485-1494 (2011) - [j18]Ya-Jun Qu, Bao-Gang Hu:
Generalized Constraint Neural Network Regression Model Subject to Linear Priors. IEEE Trans. Neural Networks 22(12): 2447-2459 (2011) - [c40]Ning Wang, Bao-Gang Hu:
IdiotPencil: An Interactive System for Generating Pencil Drawings from 3D Polygonal Models. CAD/Graphics 2011: 367-374 - [c39]Ran He, Wei-Shi Zheng, Bao-Gang Hu, Xiangwei Kong:
Nonnegative sparse coding for discriminative semi-supervised learning. CVPR 2011: 2849-2856 - [c38]Baoyuan Wu, Bao-Gang Hu:
Density and neighbor Adaptive Information Theoretic Clustering. IJCNN 2011: 230-237 - [i3]Bao-Gang Hu:
What are the Differences between Bayesian Classifiers and Mutual-Information Classifiers? CoRR abs/1105.0051 (2011) - [i2]Bao-Gang Hu, Ran He, Xiaotong Yuan:
Information-Theoretic Measures for Objective Evaluation of Classifications. CoRR abs/1107.1837 (2011) - 2010
- [j17]Ran He, Bao-Gang Hu, Xiaotong Yuan, Wei-Shi Zheng:
Principal component analysis based on non-parametric maximum entropy. Neurocomputing 73(10-12): 1840-1852 (2010) - [c37]Ran He, Bao-Gang Hu, Wei-Shi Zheng, Yanqing Guo:
Two-Stage Sparse Representation for Robust Recognition on Large-Scale Database. AAAI 2010 - [c36]Bo Dai, Bao-Gang Hu, Gang Niu:
Bayesian Maximum Margin Clustering. ICDM 2010: 108-117 - [c35]Yajun Qu, Bo Dai, Bao-Gang Hu:
Neural-network based regression model with prior from ranking information. IJCNN 2010: 1-8 - [c34]Bo Dai, Bao-Gang Hu:
Minimum Conditional Entropy Clustering: A Discriminative Framework for Clustering. ACML 2010: 47-62
2000 – 2009
- 2009
- [j16]Han-Bing Qu, Bao-Gang Hu:
Variational learning for Generalized Associative Functional Networks in modeling dynamic process of plant growth. Ecol. Informatics 4(3): 163-176 (2009) - [j15]Bao-Gang Hu, Han-Bing Qu, Yong Wang, Shuang-Hong Yang:
A generalized-constraint neural network model: Associating partially known relationships for nonlinear regressions. Inf. Sci. 179(12): 1929-1943 (2009) - [j14]Hong-Jie Xing, Bao-Gang Hu:
Two-Phase Construction of Multilayer Perceptrons Using Information Theory. IEEE Trans. Neural Networks 20(4): 715-721 (2009) - [c33]Ran He, Bao-Gang Hu, Xiaotong Yuan:
Robust Discriminant Analysis Based on Nonparametric Maximum Entropy. ACML 2009: 120-134 - [c32]Yong Wang, Bao-Gang Hu:
Derivations of Normalized Mutual Information in Binary Classifications. FSKD (1) 2009: 155-163 - [c31]Ya-Jun Qu, Bao-Gang Hu:
RBF networks for nonlinear models subject to linear constraints. GrC 2009: 482-487 - [c30]Xiaotong Yuan, Bao-Gang Hu:
Robust feature extraction via information theoretic learning. ICML 2009: 1193-1200 - [c29]Shuang-Hong Yang, Hongyuan Zha, Bao-Gang Hu:
Dirichlet-Bernoulli Alignment: A Generative Model for Multi-Class Multi-Label Multi-Instance Corpora. NIPS 2009: 2143-2150 - [c28]Shuang-Hong Yang, Hongyuan Zha, Shaohua Kevin Zhou, Bao-Gang Hu:
Variational Graph Embedding for Globally and Locally Consistent Feature Extraction. ECML/PKDD (2) 2009: 538-553 - [c27]Xiaotong Yuan, Bao-Gang Hu, Ran He:
Agglomerative Mean-Shift Clustering via Query Set Compression. SDM 2009: 223-234 - 2008
- [j13]Hong-Jie Xing, Bao-Gang Hu:
An adaptive fuzzy c-means clustering-based mixtures of experts model for unlabeled data classification. Neurocomputing 71(4-6): 1008-1021 (2008) - [j12]Shuang-Hong Yang, Bao-Gang Hu, Paul-Henry Cournède:
Structural identifiability of generalized constraint neural network models for nonlinear regression. Neurocomputing 72(1-3): 392-400 (2008) - [j11]Meng-Zhen Kang, Paul-Henry Cournède, Philippe de Reffye, D. Auclair, Bao-Gang Hu:
Analytical study of a stochastic plant growth model: Application to the GreenLab model. Math. Comput. Simul. 78(1): 57-75 (2008) - [c26]Shuang-Hong Yang, Bao-Gang Hu:
Efficient Feature Selection in the Presence of Outliers and Noises. AIRS 2008: 184-191 - [c25]Xing Mei, Philippe Decaudin, Bao-Gang Hu, Xiaopeng Zhang:
Real-Time Marker Level Set on GPU. CW 2008: 209-216 - [c24]Bao-Gang Hu, Xiaopeng Zhang, Gang Yang, Marc Jaeger:
Objective Evaluation of 3D Reconstructed Plants and Trees from 2D Images. CW 2008: 263-270 - [c23]Chao Zhu, Xiaopeng Zhang, Bao-Gang Hu, Marc Jaeger:
Reconstruction of Tree Crown Shape from Scanned Data. Edutainment 2008: 745-756 - [c22]Hong-Jie Xing, Minghu Ha, Da-Zeng Tian, Bao-Gang Hu:
A novel support vector machine with its features weighted by mutual information. IJCNN 2008: 315-320 - [c21]Shuang-Hong Yang, Bao-Gang Hu:
Feature Selection by Nonparametric Bayes Error Minimization. PAKDD 2008: 417-428 - [c20]Shuang-Hong Yang, Yujiu Yang, Bao-Gang Hu:
Sparse Kernel-Based Feature Weighting. PAKDD 2008: 813-820 - [c19]Yujiu Yang, Shuang-Hong Yang, Bao-Gang Hu:
Fighting WebSpam: Detecting Spam on the Graph Via Content and Link Features. PAKDD 2008: 1049-1055 - [c18]Shuang-Hong Yang, Bao-Gang Hu