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    Browsing by Author "Hu, Minghui"

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        Automated layer-wise solution for ensemble deep randomized feed-forward neural network 

        Hu, Minghui; Gao, Ruobin; Suganthan, Ponnuthurai N.; Tanveer, M. ( Elsevier B.V. , 2022 , Article)
        The randomized feed-forward neural network is a single hidden layer feed-forward neural network that enables efficient learning by optimizing only the output weights. The ensemble deep learning framework significantly ...
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        Deep Reservoir Computing Based Random Vector Functional Link for Non-sequential Classification 

        Hu, Minghui; Gao, Ruobin; Suganthan, P. N. ( Institute of Electrical and Electronics Engineers Inc. , 2022 , Conference)
        Reservoir Computing (RC) is well-suited for simpler sequential tasks which require inexpensive, rapid training, and the Echo State Network (ESN) plays a significant role in RC. In this article, we proposed variations of ...
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        Ensemble deep learning: A review 

        Ganaie, M. A.; Hu, Minghui; Malik, A. K.; Tanveer, M.; Suganthan, P. N. ( Elsevier Ltd , 2022 , Other)
        Ensemble learning combines several individual models to obtain better generalization performance. Currently, deep learning architectures are showing better performance compared to the shallow or traditional models. Deep ...
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        Experimental evaluation of stochastic configuration networks: Is SC algorithm inferior to hyper-parameter optimization method? 

        Hu, Minghui; Suganthan, P. N. ( Elsevier Ltd , 2022 , Article)
        To overcome the pitfalls of Random Vector Functional Link (RVFL), a network called Stochastic Configuration Networks (SCN) has been proposed. By constraining and adaptively selecting the range of randomized parameters using ...
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        Representation learning using deep random vector functional link networks for clustering: Representation learning using deep RVFL for clustering 

        Hu, Minghui; Suganthan, P. N. ( Elsevier Ltd , 2022 , Article)
        Random Vector Functional Link (RVFL) Networks have received a lot of attention due to the fast training speed as the non-iterative solution characteristic. Currently, the main research direction of RVFLs has supervised ...
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        Significant wave height forecasting using hybrid ensemble deep randomized networks with neurons pruning 

        Gao, Ruobin; Li, Ruilin; Hu, Minghui; Suganthan, Ponnuthurai Nagaratnam; Yuen, Kum Fai ( Elsevier Ltd , 2023 , Article)
        The reliable control of wave energy devices highly relies on the forecasts of wave heights. However, the dynamic characteristics and significant fluctuation of waves’ historical data pose challenges to precise predictions. ...
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        Stacked Ensemble Deep Random Vector Functional Link Network with Residual Learning for Medium-Scale Time-Series Forecasting 

        Gao, Ruobin; Hu, Minghui; Li, Ruilin; Luo, Xuewen; Suganthan, Ponnuthurai Nagaratnam; Tanveer, M.... more authors ... less authors ( Institute of Electrical and Electronics Engineers Inc. (IEEE) , 2025 , Article)
        The deep random vector functional link (dRVFL) and ensemble dRVFL (edRVFL) succeed in various tasks and achieve state-of-the-art performance compared with other randomized neural networks (NNs). However, existing edRVFL ...
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        Weighting and pruning based ensemble deep random vector functional link network for tabular data classification 

        Shi, Qiushi; Hu, Minghui; Suganthan, Ponnuthurai Nagaratnam; Katuwal, Rakesh ( Elsevier Ltd , 2022 , Article)
        In this paper, we first integrate normalization to the Ensemble Deep Random Vector Functional Link network (edRVFL). This re-normalization step can help the network avoid divergence of the hidden features. Then, we propose ...

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