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ORCIDGuangquan Zhang 0001
Person information
- affiliation: University of Technology Sydney, Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, NSW, Australia
- affiliation (PhD 2000): Curtin University of Technology, Department of Mathematics and Statistics, Perth, WA, Australia
- affiliation (1979 - 1997): Hebei University, Baoding, China
Other persons with the same name
- Guangquan Zhang 0002 — Soochow University, Suzhou, China
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2020 – today
- 2023
[j186]Zhen Fang
, Jie Lu, Feng Liu, Guangquan Zhang:
Semi-Supervised Heterogeneous Domain Adaptation: Theory and Algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 45(1): 1087-1105 (2023)- [j185]Hang Yu, Weixu Liu, Jie Lu, Yimin Wen, Xiangfeng Luo, Guangquan Zhang:
Detecting group concept drift from multiple data streams. Pattern Recognit. 134: 109113 (2023) - 2022
- [j184]Bin Wang, Jie Lu, Tianrui Li, Zheng Yan, Guangquan Zhang:
A quantile fusion methodology for deep forecasting. Neurocomputing 483: 286-298 (2022) - [j183]Kun Wang, Jie Lu, Anjin Liu, Yiliao Song, Li Xiong, Guangquan Zhang:
Elastic gradient boosting decision tree with adaptive iterations for concept drift adaptation. Neurocomputing 491: 288-304 (2022) - [j182]En Yu, Yiliao Song, Guangquan Zhang, Jie Lu:
Learn-to-adapt: Concept drift adaptation for hybrid multiple streams. Neurocomputing 496: 121-130 (2022) - [j181]Kai Yang, Jie Lu, Wanggen Wan, Guangquan Zhang, Li Hou:
Transfer learning based on sparse Gaussian process for regression. Inf. Sci. 605: 286-300 (2022) - [j180]Hang Yu, Qingyong Zhang, Tianyu Liu, Jie Lu, Yimin Wen, Guangquan Zhang:
Meta-ADD: A meta-learning based pre-trained model for concept drift active detection. Inf. Sci. 608: 996-1009 (2022) - [j179]Hang Yu, Jie Lu, Guangquan Zhang:
Continuous Support Vector Regression for Nonstationary Streaming Data. IEEE Trans. Cybern. 52(5): 3592-3605 (2022) - [j178]Fan Dong, Jie Lu, Yiliao Song, Feng Liu, Guangquan Zhang:
A Drift Region-Based Data Sample Filtering Method. IEEE Trans. Cybern. 52(9): 9377-9390 (2022) - [j177]Hang Yu, Jie Lu, Guangquan Zhang:
Topology Learning-Based Fuzzy Random Neural Networks for Streaming Data Regression. IEEE Trans. Fuzzy Syst. 30(2): 412-425 (2022) - [j176]Hang Yu, Jie Lu, Anjin Liu, Bin Wang, Ruimin Li, Guangquan Zhang:
Real-Time Prediction System of Train Carriage Load Based on Multi-Stream Fuzzy Learning. IEEE Trans. Intell. Transp. Syst. 23(9): 15155-15165 (2022) - [j175]Hang Yu, Jie Lu, Guangquan Zhang:
An Online Robust Support Vector Regression for Data Streams. IEEE Trans. Knowl. Data Eng. 34(1): 150-163 (2022) - [j174]Yiliao Song, Jie Lu, Anjin Liu, Haiyan Lu, Guangquan Zhang:
A Segment-Based Drift Adaptation Method for Data Streams. IEEE Trans. Neural Networks Learn. Syst. 33(9): 4876-4889 (2022) - [j173]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-Source Contribution Learning for Domain Adaptation. IEEE Trans. Neural Networks Learn. Syst. 33(10): 5293-5307 (2022) - [j172]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning From a Complementary-Label Source Domain: Theory and Algorithms. IEEE Trans. Neural Networks Learn. Syst. 33(12): 7667-7681 (2022) - [j171]Hang Yu, Jie Lu, Guangquan Zhang:
MORStreaming: A Multioutput Regression System for Streaming Data. IEEE Trans. Syst. Man Cybern. Syst. 52(8): 4862-4874 (2022) - [c148]Zhaoqing Liu, Anjin Liu, Guangquan Zhang, Jie Lu:
An Empirical Study of Fuzzy Decision Tree for Gradient Boosting Ensemble. AI 2022: 716-727 - [c147]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Source-Free Multi-Domain Adaptation with Generally Auxiliary Model Training. IJCNN 2022: 1-8 - [i26]Guangzhi Ma, Jie Lu, Feng Liu, Zhen Fang, Guangquan Zhang:
Multi-class Classification with Fuzzy-feature Observations: Theory and Algorithms. CoRR abs/2206.04311 (2022) - [i25]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
Streaming PAC-Bayes Gaussian process regression with a performance guarantee for online decision making. CoRR abs/2210.08486 (2022) - 2021
- [j170]Wenjun Chang, Qian Zhang, Chao Fu, Weiyong Liu, Guangquan Zhang, Jie Lu:
A cross-domain recommender system through information transfer for medical diagnosis. Decis. Support Syst. 143: 113489 (2021) - [j169]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
Statistical generalization performance guarantee for meta-learning with data dependent prior. Neurocomputing 465: 391-405 (2021) - [j168]Qian Liu, Jie Lu, Guangquan Zhang, Tao Shen, Zhihan Zhang, Heyan Huang:
Domain-specific meta-embedding with latent semantic structures. Inf. Sci. 555: 410-423 (2021) - [j167]Kai Yang, Jie Lu, Wanggen Wan, Guangquan Zhang:
Multi-source transfer regression via source-target pairwise segment. Inf. Sci. 556: 389-403 (2021) - [j166]Yi Zhang, Mengjia Wu, George Yijun Tian, Guangquan Zhang, Jie Lu:
Ethics and privacy of artificial intelligence: Understandings from bibliometrics. Knowl. Based Syst. 222: 106994 (2021) - [j165]Anjin Liu, Jie Lu, Guangquan Zhang:
Concept Drift Detection via Equal Intensity k-Means Space Partitioning. IEEE Trans. Cybern. 51(6): 3198-3211 (2021) - [j164]Qian Liu, Heyan Huang, Junyu Xuan, Guangquan Zhang, Yang Gao, Jie Lu:
A Fuzzy Word Similarity Measure for Selecting Top-$k$ Similar Words in Query Expansion. IEEE Trans. Fuzzy Syst. 29(8): 2132-2144 (2021) - [j163]Anjin Liu, Jie Lu, Guangquan Zhang:
Concept Drift Detection: Dealing With Missing Values via Fuzzy Distance Estimations. IEEE Trans. Fuzzy Syst. 29(11): 3219-3233 (2021) - [j162]Feng Liu, Guangquan Zhang, Jie Lu:
Multisource Heterogeneous Unsupervised Domain Adaptation via Fuzzy Relation Neural Networks. IEEE Trans. Fuzzy Syst. 29(11): 3308-3322 (2021) - [j161]Junyu Xuan, Jie Lu, Guangquan Zhang:
Bayesian Nonparametric Unsupervised Concept Drift Detection for Data Stream Mining. ACM Trans. Intell. Syst. Technol. 12(1): 5:1-5:22 (2021) - [j160]Anjin Liu, Jie Lu, Guangquan Zhang:
Diverse Instance-Weighting Ensemble Based on Region Drift Disagreement for Concept Drift Adaptation. IEEE Trans. Neural Networks Learn. Syst. 32(1): 293-307 (2021) - [j159]Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang:
Open Set Domain Adaptation: Theoretical Bound and Algorithm. IEEE Trans. Neural Networks Learn. Syst. 32(10): 4309-4322 (2021) - [c146]Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang:
How Does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? AAAI 2021: 11079-11087 - [c145]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-Source Domain Adaptation with Fuzzy-Rule based Deep Neural Networks. FUZZ-IEEE 2021: 1-6 - [c144]Guangzhi Ma, Feng Liu, Guangquan Zhang, Jie Lu:
Learning from Imprecise Observations: An Estimation Error Bound based on Fuzzy Random Variables. FUZZ-IEEE 2021: 1-8 - [c143]Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. ICML 2021: 3122-3132 - [c142]Ming Zhou, Yiliao Song, Guangquan Zhang, Bin Zhang, Jie Lu:
An Efficient Bayesian Neural Network for Multiple Data Streams. IJCNN 2021: 1-8 - [i24]Li Zhong, Zhen Fang, Feng Liu, Jie Lu, Bo Yuan, Guangquan Zhang:
How does the Combined Risk Affect the Performance of Unsupervised Domain Adaptation Approaches? CoRR abs/2101.01104 (2021) - [i23]Tianyu Liu, Jie Lu, Zheng Yan, Guangquan Zhang:
PAC-Bayes Bounds for Meta-learning with Data-Dependent Prior. CoRR abs/2102.03748 (2021) - [i22]Hang Yu, Tianyu Liu, Jie Lu, Guangquan Zhang:
Automatic Learning to Detect Concept Drift. CoRR abs/2105.01419 (2021) - [i21]Zhen Fang, Jie Lu, Anjin Liu, Feng Liu, Guangquan Zhang:
Learning Bounds for Open-Set Learning. CoRR abs/2106.15792 (2021) - [i20]Adi Lin, Jie Lu, Junyu Xuan, Fujin Zhu, Guangquan Zhang:
Deep Bayesian Estimation for Dynamic Treatment Regimes with a Long Follow-up Time. CoRR abs/2109.11929 (2021) - [i19]Junyu Xuan, Jie Lu, Guangquan Zhang:
Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning. CoRR abs/2109.13233 (2021) - 2020
- [b4]Jie Lu, Qian Zhang, Guangquan Zhang:
Recommender Systems - Advanced Developments. Intelligent Information Systems 6, WorldScientific 2020, ISBN 9789811224621, pp. 1-380 - [j158]Yi Zhang, Mengjia Wu, Hua Lin, Steven Tipper, Mark Grosser, Guangquan Zhang, Jie Lu:
Framework of Computational Intelligence-Enhanced Knowledge Base Construction: Methodology and A Case of Gene-Related Cardiovascular Disease. Int. J. Comput. Intell. Syst. 13(1): 1109-1119 (2020) - [j157]Fujin Zhu, Jie Lu, Adi Lin, Guangquan Zhang:
A Pareto-smoothing method for causal inference using generalized Pareto distribution. Neurocomputing 378: 142-152 (2020) - [j156]Guanjin Wang, Guangquan Zhang, Kup-Sze Choi, Kin-Man Lam, Jie Lu:
Output based transfer learning with least squares support vector machine and its application in bladder cancer prognosis. Neurocomputing 387: 279-292 (2020) - [j155]Bin Wang, Tianrui Li, Zheng Yan, Guangquan Zhang, Jie Lu:
DeepPIPE: A distribution-free uncertainty quantification approach for time series forecasting. Neurocomputing 397: 11-19 (2020) - [j154]Guanjin Wang, Jie Lu, Kup-Sze Choi, Guangquan Zhang:
A Transfer-Based Additive LS-SVM Classifier for Handling Missing Data. IEEE Trans. Cybern. 50(2): 739-752 (2020) - [j153]Yiliao Song, Jie Lu, Haiyan Lu, Guangquan Zhang:
Fuzzy Clustering-Based Adaptive Regression for Drifting Data Streams. IEEE Trans. Fuzzy Syst. 28(3): 544-557 (2020) - [j152]Jie Lu, Hua Zuo, Guangquan Zhang:
Fuzzy Multiple-Source Transfer Learning. IEEE Trans. Fuzzy Syst. 28(12): 3418-3431 (2020) - [j151]Adi Lin, Jie Lu, Junyu Xuan, Fujin Zhu, Guangquan Zhang:
A Causal Dirichlet Mixture Model for Causal Inference from Observational Data. ACM Trans. Intell. Syst. Technol. 11(3): 33:1-33:29 (2020) - [j150]Hang Yu, Jie Lu, Guangquan Zhang:
Online Topology Learning by a Gaussian Membership-Based Self-Organizing Incremental Neural Network. IEEE Trans. Neural Networks Learn. Syst. 31(10): 3947-3961 (2020) - [j149]Feng Liu, Guangquan Zhang, Jie Lu:
Heterogeneous Domain Adaptation: An Unsupervised Approach. IEEE Trans. Neural Networks Learn. Syst. 31(12): 5588-5602 (2020) - [j148]Junyu Xuan, Xiangfeng Luo, Jie Lu, Guangquan Zhang:
Web event evolution trend prediction based on its computational social context. World Wide Web 23(3): 1861-1886 (2020) - [c141]Hang Yu, Anjin Liu, Bin Wang, Ruimin Li, Guangquan Zhang, Jie Lu:
Real-Time Decision Making for Train Carriage Load Prediction via Multi-stream Learning. Australasian Conference on Artificial Intelligence 2020: 29-41 - [c140]Kun Wang, Anjin Liu, Jie Lu, Guangquan Zhang, Li Xiong:
An Elastic Gradient Boosting Decision Tree for Concept Drift Learning. Australasian Conference on Artificial Intelligence 2020: 420-432 - [c139]Feng Liu, Guangquan Zhang, Jie Lu:
A Novel Non-parametric Two-Sample Test on Imprecise Observations. FUZZ-IEEE 2020: 1-6 - [c138]Yiliao Song, Guangquan Zhang, Haiyan Lu, Jie Lu:
A Fuzzy Drift Correlation Matrix for Multiple Data Stream Regression. FUZZ-IEEE 2020: 1-6 - [c137]Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. ICML 2020: 6316-6326 - [c136]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. IJCAI 2020: 2526-2532 - [c135]Qian Zhang, Jie Lu, Guangquan Zhang:
Cross-Domain Recommendation with Multiple Sources. IJCNN 2020: 1-7 - [c134]Keqiuyin Li, Jie Lu, Hua Zuo, Guangquan Zhang:
Multi-Source Domain Adaptation with Distribution Fusion and Relationship Extraction. IJCNN 2020: 1-6 - [c133]Anjin Liu, Guangquan Zhang, Kun Wang, Jie Lu:
Fast Switch Naïve Bayes to Avoid Redundant Update for Concept Drift Learning. IJCNN 2020: 1-7 - [c132]Hua Zuo, Jie Lu, Guangquan Zhang:
Multiple-source Domain Adaptation in Rule-based Neural Network. IJCNN 2020: 1-6 - [i18]Feng Liu, Wenkai Xu, Jie Lu, Guangquan Zhang, Arthur Gretton, Danica J. Sutherland:
Learning Deep Kernels for Non-Parametric Two-Sample Tests. CoRR abs/2002.09116 (2020) - [i17]Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang:
Learning under Concept Drift: A Review. CoRR abs/2004.05785 (2020) - [i16]Anjin Liu, Jie Lu, Guangquan Zhang:
Diverse Instances-Weighting Ensemble based on Region Drift Disagreement for Concept Drift Adaptation. CoRR abs/2004.05810 (2020) - [i15]Anjin Liu, Jie Lu, Guangquan Zhang:
Concept Drift Detection via Equal Intensity k-means Space Partitioning. CoRR abs/2004.11587 (2020) - [i14]Li Zhong, Zhen Fang, Feng Liu, Bo Yuan, Guangquan Zhang, Jie Lu:
Bridging the Theoretical Bound and Deep Algorithms for Open Set Domain Adaptation. CoRR abs/2006.13022 (2020) - [i13]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation. CoRR abs/2007.14612 (2020) - [i12]Yiyang Zhang, Feng Liu, Zhen Fang, Bo Yuan, Guangquan Zhang, Jie Lu:
Learning from a Complementary-label Source Domain: Theory and Algorithms. CoRR abs/2008.01454 (2020) - [i11]Anjin Liu, Jie Lu, Guangquan Zhang:
Concept Drift Detection: Dealing with MissingValues via Fuzzy Distance Estimations. CoRR abs/2008.03662 (2020)
2010 – 2019
- 2019
- [j147]Junyu Xuan, Jie Lu, Guangquan Zhang:
Cooperative hierarchical Dirichlet processes: Superposition vs. maximization. Artif. Intell. 271: 43-73 (2019) - [j146]Junyu Xuan, Jie Lu, Guangquan Zhang:
A Survey on Bayesian Nonparametric Learning. ACM Comput. Surv. 52(1): 13:1-13:36 (2019) - [j145]Ximeng Wang, Yun Liu, Jie Lu, Fei Xiong, Guangquan Zhang:
TruGRC: Trust-Aware Group Recommendation with Virtual Coordinators. Future Gener. Comput. Syst. 94: 224-236 (2019) - [j144]Mingsong Mao, Jie Lu, Jialin Han, Guangquan Zhang:
Multiobjective e-commerce recommendations based on hypergraph ranking. Inf. Sci. 471: 269-287 (2019) - [j143]Ruiping Yin, Kan Li, Guangquan Zhang, Jie Lu:
A deeper graph neural network for recommender systems. Knowl. Based Syst. 185 (2019) - [j142]Shan Xue, Jie Lu, Guangquan Zhang:
Cross-domain network representations. Pattern Recognit. 94: 135-148 (2019) - [j141]Peng Hao, Guangquan Zhang, Luis Martínez-López, Jie Lu:
Regularizing Knowledge Transfer in Recommendation With Tag-Inferred Correlation. IEEE Trans. Cybern. 49(1): 83-96 (2019) - [j140]Jie Lu, Zheng Yan, Jialin Han, Guangquan Zhang:
Data-Driven Decision-Making (D3M): Framework, Methodology, and Directions. IEEE Trans. Emerg. Top. Comput. Intell. 3(4): 286-296 (2019) - [j139]Hua Zuo, Jie Lu, Guangquan Zhang, Feng Liu:
Fuzzy Transfer Learning Using an Infinite Gaussian Mixture Model and Active Learning. IEEE Trans. Fuzzy Syst. 27(2): 291-303 (2019) - [j138]Hua Zuo, Jie Lu, Guangquan Zhang, Witold Pedrycz:
Fuzzy Rule-Based Domain Adaptation in Homogeneous and Heterogeneous Spaces. IEEE Trans. Fuzzy Syst. 27(2): 348-361 (2019) - [j137]Jie Lu, Anjin Liu, Fan Dong, Feng Gu, João Gama, Guangquan Zhang:
Learning under Concept Drift: A Review. IEEE Trans. Knowl. Data Eng. 31(12): 2346-2363 (2019) - [j136]Qian Zhang, Jie Lu, Dianshuang Wu, Guangquan Zhang:
A Cross-Domain Recommender System With Kernel-Induced Knowledge Transfer for Overlapping Entities. IEEE Trans. Neural Networks Learn. Syst. 30(7): 1998-2012 (2019) - [j135]Wei Wang, Guangquan Zhang, Jie Lu:
Hierarchy Visualization for Group Recommender Systems. IEEE Trans. Syst. Man Cybern. Syst. 49(6): 1152-1163 (2019) - [j134]Guanjin Wang, Guangquan Zhang, Kup-Sze Choi, Jie Lu:
Deep Additive Least Squares Support Vector Machines for Classification With Model Transfer. IEEE Trans. Syst. Man Cybern. Syst. 49(7): 1527-1540 (2019) - [c131]Feng Liu, Guangquan Zhang, Jie Lu:
A Novel Fuzzy Neural Network for Unsupervised Domain Adaptation in Heterogeneous Scenarios. FUZZ-IEEE 2019: 1-6 - [c130]Yiliao Song, Guangquan Zhang, Haiyan Lu, Jie Lu:
A Noise-tolerant Fuzzy c-Means based Drift Adaptation Method for Data Stream Regression. FUZZ-IEEE 2019: 1-6 - [c129]Hua Zuo, Guangquan Zhang, Witold Pedrycz, Jie Lu:
Domain Selection of Transfer Learning in Fuzzy Prediction Models. FUZZ-IEEE 2019: 1-6 - [c128]Adi Lin, Jie Lu, Junyu Xuan, Fujin Zhu, Guangquan Zhang:
One-Stage Deep Instrumental Variable Method for Causal Inference from Observational Data. ICDM 2019: 419-428 - [c127]Hua Zuo, Guangquan Zhang, Jie Lu:
Fuzzy Transfer Learning in Heterogeneous Space Using Takagi-Sugeno Fuzzy Models. IFSA/NAFIPS 2019: 752-763 - [c126]Dennis Chow, Anjin Liu, Guangquan Zhang, Jie Lu:
Knowledge graph-based entity importance learning for multi-stream regression on Australian fuel price forecasting. IJCNN 2019: 1-8 - [c125]Zhen Fang, Jie Lu, Feng Liu, Guangquan Zhang:
Unsupervised Domain Adaptation with Sphere Retracting Transformation. IJCNN 2019: 1-8 - [c124]Ruiping Yin, Kan Li, Jie Lu, Guangquan Zhang:
RsyGAN: Generative Adversarial Network for Recommender Systems. IJCNN 2019: 1-7 - [c123]Hang Yu, Jie Lu, Jialu Xu, Guangquan Zhang:
A Hybrid Incremental Regression Neural Network for Uncertain Data Streams. IJCNN 2019: 1-8 - [c122]Qian Zhang, Peng Hao, Jie Lu, Guangquan Zhang:
Cross-domain Recommendation with Semantic Correlation in Tagging Systems. IJCNN 2019: 1-8 - [c121]Bin Zhang, Jie Lu, Guangquan Zhang:
Drift Adaptation via Joint Distribution Alignment. ISKE 2019: 498-504 - [c120]Qian Zhang, Di Zhang, Jie Lu, Guangquan Zhang, Wei Qu, Mark Cohen:
A Recommender System for Cold-start Items: A Case Study in the Real Estate Industry. ISKE 2019: 1185-1192 - [c119]Yi Zhang, Yihe Zhu, Lu Huang, Guangquan Zhang, Jie Lu:
Characterizing the Potential of Being Emerging Generic Technologies: A Bi-Layer Network Analytics-based Prediction Method. ISSI 2019: 1436-1447 - [c118]Bin Wang, Jie Lu, Zheng Yan, Huaishao Luo, Tianrui Li, Yu Zheng, Guangquan Zhang:
Deep Uncertainty Quantification: A Machine Learning Approach for Weather Forecasting. KDD 2019: 2087-2095 - [c117]Ruiping Yin, Kan Li, Jie Lu, Guangquan Zhang:
Enhancing Fashion Recommendation with Visual Compatibility Relationship. WWW 2019: 3434-3440 - [i10]Feng Liu, Jie Lu, Bo Han, Gang Niu, Guangquan Zhang, Masashi Sugiyama:
Butterfly: A Panacea for All Difficulties in Wildly Unsupervised Domain Adaptation. CoRR abs/1905.07720 (2019) - [i9]Zhen Fang, Jie Lu, Feng Liu, Junyu Xuan, Guangquan Zhang:
Open Set Domain Adaptation: Theoretical Bound and Algorithm. CoRR abs/1907.08375 (2019) - [i8]Shan Xue, Jie Lu, Guangquan Zhang:
Cross-domain Network Representations. CoRR abs/1908.00205 (2019) - [i7]Shan Xue, Jie Lu, Guangquan Zhang, Li Xiong:
A Framework of Transferring Structures Across Large-scale Information Networks. CoRR abs/1911.04665 (2019) - 2018
- [j133]Fan Dong, Jie Lu, Guangquan Zhang, Kan Li:
Active Fuzzy Weighting Ensemble for Dealing with Concept Drift. Int. J. Comput. Intell. Syst. 11(1): 438-450 (2018) - [j132]Junyu Xuan, Jie Lu, Zheng Yan, Guangquan Zhang:
Bayesian Deep Reinforcement Learning via Deep Kernel Learning. Int. J. Comput. Intell. Syst. 12(1): 164-171 (2018) - [j131]Yue Zheng, Guangquan Zhang, Zhen Zhang, Jie Lu:
A reducibility method for the weak linear bilevel programming problems and a case study in principal-agent. Inf. Sci. 454-455: 46-58 (2018) - [j130]Yi Zhang, Jie Lu, Feng Liu, Qian Liu, Alan L. Porter, Hongshu Chen, Guangquan Zhang:
Does deep learning help topic extraction? A kernel k-means clustering method with word embedding. J. Informetrics 12(4): 1099-1117 (2018) - [j129]Fan Dong, Guangquan Zhang, Jie Lu, Kan Li:
Fuzzy competence model drift detection for data-driven decision support systems. Knowl. Based Syst. 143: 284-294 (2018) - [j128]Jie Lu, Junyu Xuan, Guangquan Zhang, Xiangfeng Luo:
Structural property-aware multilayer network embedding for latent factor analysis. Pattern Recognit. 76: 228-241 (2018) - [j127]Anjin Liu, Jie Lu, Feng Liu, Guangquan Zhang:
Accumulating regional density dissimilarity for concept drift detection in data streams. Pattern Recognit. 76: 256-272 (2018) - [j126]Hua Zuo, Guangquan Zhang, Witold Pedrycz, Vahid Behbood, Jie Lu:
Granular Fuzzy Regression Domain Adaptation in Takagi-Sugeno Fuzzy Models. IEEE Trans. Fuzzy Syst. 26(2): 847-858 (2018) - [j125]Feng Liu, Jie Lu, Guangquan Zhang:
Unsupervised Heterogeneous Domain Adaptation via Shared Fuzzy Equivalence Relations. IEEE Trans. Fuzzy Syst. 26(6): 3555-3568 (2018) - [j124]Tinghua Wang, Jie Lu, Guangquan Zhang:
Two-Stage Fuzzy Multiple Kernel Learning Based on Hilbert-Schmidt Independence Criterion. IEEE Trans. Fuzzy Syst. 26(6): 3703-3714 (2018) - [j123]Junyu Xuan, Jie Lu, Guangquan Zhang, Richard Yi Da Xu, Xiangfeng Luo:
Doubly Nonparametric Sparse Nonnegative Matrix Factorization Based on Dependent Indian Buffet Processes. IEEE Trans. Neural Networks Learn. Syst. 29(5): 1835-1849 (2018) - [j122]