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ALR-HT: A fast and efficient Lasso regression without hyperparameter tuning Journal article
Wang, Yuhang, Zou, Bin, Xu, Jie, Xu, Chen, Tang, Yuan Yan. ALR-HT: A fast and efficient Lasso regression without hyperparameter tuning[J]. Neural Networks, 2025, 181.
Authors:  Wang, Yuhang;  Zou, Bin;  Xu, Jie;  Xu, Chen;  Tang, Yuan Yan
Favorite | TC[WOS]:0 TC[Scopus]:0  IF:6.0/7.9 | Submit date：2025/01/22
Additive Models  Generalization Bound  Hyperparameter Tuning  Lasso Regression  Markov Resampling  Ridge Regression  
Probabilistic Nuclear-Norm Matrix Regression Regularized by Random Graph Theory Journal article
Zhou, Jianhang, Li, Shuyi, Zeng, Shaoning, Zhang, Bob. Probabilistic Nuclear-Norm Matrix Regression Regularized by Random Graph Theory[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024.
Authors:  Zhou, Jianhang;  Li, Shuyi;  Zeng, Shaoning;  Zhang, Bob
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:5.3/5.7 | Submit date：2024/05/16
Adaptation Models  Bayes Methods  Computational Intelligence  Computational Intelligence  Graph Theory  Nuclear Magnetic Resonance  Nuclear-norm Matrix Regression  Probabilistic Logic  Probability Theory  Random Graph  Structural Information  Training  
Extreme Fuzzy Broad Learning System: Algorithm, Frequency Principle, and Applications in Classification and Regression Journal article
Duan, Junwei, Yao, Shiyi, Tan, Jiantao, Liu, Yang, Chen, Long, Zhang, Zhen, Chen, C. L.P.. Extreme Fuzzy Broad Learning System: Algorithm, Frequency Principle, and Applications in Classification and Regression[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024.
Authors:  Duan, Junwei;  Yao, Shiyi;  Tan, Jiantao;  Liu, Yang;  Chen, Long; et al.
Favorite | TC[WOS]:3 TC[Scopus]:2  IF:10.2/10.4 | Submit date：2024/05/16
Broad Learning System (Bls)  Classification  Deep Neural Network  Feature Extraction  Frequency Principle  Fuzzy Extreme Learning Machine (Elm)  Learning Systems  Mathematical Models  Neural Networks  Regression  Stacking  Task Analysis  Training  
Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models Journal article
Chen, Guang Yong, Gan, Min, Chen, Jing, Chen, Long. Embedded Point Iteration Based Recursive Algorithm for Online Identification of Nonlinear Regression Models[J]. IEEE Transactions on Automatic Control, 2022, 68(7), 4257-4264.
Authors:  Chen, Guang Yong;  Gan, Min;  Chen, Jing;  Chen, Long
Favorite | TC[WOS]:11 TC[Scopus]:11  IF:6.2/6.6 | Submit date：2023/01/30
Approximation Algorithms  Couplings  Data Models  Jacobian Matrices  Nonlinear Regression Models  Numerical Models  Online Identification  Parameter Estimation  Predictive Models  Time Series Analysis  Variable Projection  
Balanced augmented empirical likelihood for regression models Journal article
Xia,Xiaochao, Liu,Zhi. Balanced augmented empirical likelihood for regression models[J]. Journal of the Korean Statistical Society, 2019, 48(2), 233-247.
Authors:  Xia,Xiaochao;  Liu,Zhi
Favorite | TC[WOS]:1 TC[Scopus]:1  IF:0.6/0.7 | Submit date：2021/03/11
Asymptotic Properties  Balanced Augmented Sample  Convex Hull Constraint  Empirical Likelihood  Regression Models  
Lasso for sparse linear regression with exponentially β-mixing errors Journal article
Xie,Fang, Xu,Lihu, Yang,Youcai. Lasso for sparse linear regression with exponentially β-mixing errors[J]. STATISTICS & PROBABILITY LETTERS, 2017, 125, 64-70.
Authors:  Xie,Fang;  Xu,Lihu;  Yang,Youcai
Favorite | TC[WOS]:5 TC[Scopus]:5 | Submit date：2019/06/03
Consistency  Exponentially Β-mixing Errors  Lasso  Linear Regression Models  
Lasso for sparse linear regression with exponentially beta-mixing errors Journal article
Xie, Fang, Xu, Lihu, Yang, Youcai. Lasso for sparse linear regression with exponentially beta-mixing errors[J]. STATISTICS & PROBABILITY LETTERS, 2017, 125, 64-70.
Authors:  Xie, Fang;  Xu, Lihu;  Yang, Youcai
Favorite | TC[WOS]:5 TC[Scopus]:5  IF:0.9/0.8 | Submit date：2018/10/30
Lasso  Linear Regression Models  Consistency  Exponentially Beta-mixing Errors  
Integrating Support Vector Regression with Particle Swarm Optimization for Numerical Modeling for Algal Blooms of Freshwater Book chapter
出自: Advances in Monitoring and Modelling Algal Blooms in Freshwater Reservoirs General Principles and a Case study of Macau:Science+Business Media Dordrecht 2017, 2017
Authors:  lnchio Lou;  Zhengchao Xie;  Wai Kin Ung;  Kai Meng Mok
Favorite | TC[Scopus]:20 | Submit date：2019/06/26
Algal Bloom  Prediction And Forecast Models  Phytoplankton Abundance  Support Vector Regression  Particle Swarm Optimization  
Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater Journal article
Lou,Inchio, Xie,Zhengchao, Ung,Wai Kin, Mok,Kai Meng. Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater[J]. Applied Mathematical Modelling, 2015, 39(19), 5907-5916.
Authors:  Lou,Inchio;  Xie,Zhengchao;  Ung,Wai Kin;  Mok,Kai Meng
Favorite | TC[WOS]:19 TC[Scopus]:20  IF:4.4/4.2 | Submit date：2021/03/09
Algal Bloom  Particle Swarm Optimization  Phytoplankton Abundance  Prediction And Forecast Models  Support Vector Regression  
Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater Journal article
Inchio Lou, Zhengchao Xie, Wai Kin Ung, Kai Meng Mok. Integrating Support Vector Regression with Particle Swarm Optimization for numerical modeling for algal blooms of freshwater[J]. Applied Mathematical Modelling, 2015, 39(19), 5907-5916.
Authors:  Inchio Lou;  Zhengchao Xie;  Wai Kin Ung;  Kai Meng Mok
Favorite | TC[WOS]:19 TC[Scopus]:20  IF:4.4/4.2 | Submit date：2019/02/12
Algal Bloom  Particle Swarm Optimization  Phytoplankton Abundance  Prediction And Forecast Models  Support Vector Regression