drwuHUST/TLBCI drwuHUST/TLBCI

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    MATLAB
  • Created over 4 years ago
  • Updated over 1 year ago
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drwuHUST/TLBCI
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Repository Details

Matlab source code of the paper "D. Wu, X. Jiang, R. Peng, W. Kong, J. Huang and Z. Zeng, Transfer Learning for Motor Imagery Based Brain-Computer Interfaces: A Complete Pipeline, Information Sciences, 2021, submitted."

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Matlab source code of the paper "D. Wu and X. Tan, Multitasking Genetic Algorithm (MTGA) for Fuzzy System Optimization, IEEE Trans. on Fuzzy Systems, 28(6), pp. 1050-1061, 2020."
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MBGD_RDA

Matlab source code of the paper "D. Wu, Y. Yuan, J. Huang and Y. Tan*, Optimize TSK Fuzzy Systems for Regression Problems: Mini-Batch Gradient Descent with Regularization, DropRule and AdaBound (MBGD-RDA), IEEE Trans. on Fuzzy Systems, 28(5), pp. 1003-1015, 2020."
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MBGD_UR_BN

Matlab source code of the paper "Y. Cui, D. Wu* and J. Huang*, "Optimize TSK Fuzzy Systems for Classification Problems: Mini-Batch Gradient Descent with Uniform Regularization and Batch Normalization," IEEE Trans. on Fuzzy Systems, 28(12):3065-3075, 2020." Python source code is available at https://github.com/YuqiCui/TSK_BN_UR
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iGS

Matlab source code of the paper: D. Wu*, C-T Lin and J. Huang*, "Active Learning for Regression Using Greedy Sampling," Information Sciences, vol. 474, pp. 90-105, 2019.
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iRDM

Matlab source code of the iRDM algorithm in the paper: Z. Liu, X. Jiang, H. Luo, W. Fang, J. Liu and D. Wu*, "Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)," Pattern Recognition Letters, 142:11-19, 2021.
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Patch-Learning

Matlab source code of the paper "D. Wu and J.M. Mendel, Patch Learning, IEEE Trans. on Fuzzy Systems, 28(9):1996-2008, 2020"
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IRD

Matlab code of the IRD algorithm in the paper: εˆ˜ε­ζ˜‚, θ’‹ι›ͺ, 伍冬睿, "εŸΊδΊŽζ± ηš„ζ— η›‘η£ηΊΏζ€§ε›žε½’δΈ»εŠ¨ε­¦δΉ ," θ‡ͺεŠ¨εŒ–ε­¦ζŠ₯, 2020. Or the English version here: https://arxiv.org/pdf/2001.05028.pdf
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sAL

Matlab source code of the paper: D. Wu, "Pool-based sequential active learning for regression," IEEE Trans. on Neural Networks and Learning Systems, 30(5), pp. 1348-1359, 2019.
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