Fetching the paper…
Reading the bibliography…
Neural networks have been successfully employed in various domains such as classification, regression and clustering, etc.
The perceptron: a probabilistic model for information storage and organization in the brain.,
F. Rosenblatt, · 1958
Earlier work this paper cites.
F. Rosenblatt, Principles of neurodynamics. perceptrons and the theory of brain mechanisms, Technical Report, Cornell Aeronautical Lab Inc Buffalo NY, 1961
1961
Earlier work this paper cites.
Further contributions to the theory of generalized inverse of matrices and its applications,
C. R. Rao, S. K. Mitra, · 1971
Earlier work this paper cites.
Fuzzy identification of systems and its applications to modeling and control,
T. Takagi, M. Sugeno, · 1985
Earlier work this paper cites.
Increased rates of convergence through learning rate adaptation,
R. A. Jacobs, · 1988
Earlier work this paper cites.
D. S. Broomhead, D. Lowe, Radial basis functions, multi-variable functional interpolation and adaptive networks, Technical Report, Royal Signals and Radar Establishment Malvern (United Kingdom), 1988
1988
Earlier work this paper cites.
A. K. Jain, R. C. Dubes, Algorithms for clustering data, Prentice-Hall, Inc., 1988
1988
Earlier work this paper cites.
Neural network architectures for robotic applications,
S.-Y. King, J.-N. Hwang, · 1989
Earlier work this paper cites.
Multilayer feedforward networks are universal approximators,
K. Hornik, M. Stinchcombe, H. White, · 1989
Earlier work this paper cites.
Non-linear system identification using neural networks,
S. Chen, S. A. Billings, P. Grant, · 1990
Earlier work this paper cites.
Universal approximation using radial-basis-function networks,
J. Park, I. W. Sandberg, · 1991
Earlier work this paper cites.
Neural networks for control systems—a survey,
K. J. Hunt, D. Sbarbaro, R. Żbikowski, P. J. Gawthrop, · 1992
Earlier work this paper cites.
On the problem of local minima in backpropagation,
M. Gori, A. Tesi, · 1992
Earlier work this paper cites.
Feed forward neural networks with random weights,
W. F. Schmidt, M. A. Kraaijveld, R. P. Duin, · 1992
Earlier work this paper cites.
Functional-link net computing: theory, system architecture, and functionalities,
Y.-H. Pao, Y. Takefuji, · 1992
Earlier work this paper cites.
Neural-net computing and the intelligent control of systems,
Y.-H. Pao, S. M. Phillips, D. J. Sobajic, · 1992
Earlier work this paper cites.
Stacked generalization,
D. H. Wolpert, · 1992
Earlier work this paper cites.
Multilayer feedforward networks with a nonpolynomial activation function can approximate any function,
M. Leshno, V. Y. Lin, A. Pinkus, S. Schocken, · 1993
Earlier work this paper cites.
Learning and generalization characteristics of the random vector functional-link net,
Y.-H. Pao, G.-H. Park, D. J. Sobajic, · 1994
Earlier work this paper cites.
Wavelet neural networks for function learning,
J. Zhang, G. G. Walter, Y. Miao, W. N. W. Lee, · 1995
Earlier work this paper cites.
Stochastic choice of basis functions in adaptive function approximation and the functional-link net,
B. Igelnik, Y.-H. Pao, · 1995
Earlier work this paper cites.
Support-vector networks,
C. Cortes, V. Vapnik, · 1995
Earlier work this paper cites.
Bagging predictors,
L. Breiman, · 1996
Earlier work this paper cites.
Experiments with a new boosting algorithm,
Y. Freund, R. E. Schapire, · 1996
Earlier work this paper cites.
Long short-term memory,
S. Hochreiter, J. Schmidhuber, · 1997
Earlier work this paper cites.
Universal approximation using feedforward neural networks: A survey of some existing methods, and some new results,
F. Scarselli, A. C. Tsoi, · 1998
Earlier work this paper cites.
GMDH algorithms for complex systems modelling,
J.-A. Mueller, A. Ivachnenko, F. Lemke, · 1998
Earlier work this paper cites.
The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis,
N. E. Huang, Z. Shen, S. R. Long, M. C. Wu, H. H. Shih, Q. Zheng, N.-C. Yen, C. C. Tung, H. H. Liu, · 1998
Earlier work this paper cites.
An incremental adaptive implementation of functional-link processing for function approximation, time-series prediction, and system identification,
C. P. Chen, S. R. LeClair, Y.-H. Pao, · 1998
Earlier work this paper cites.
Improving the convergence of the backpropagation algorithm using learning rate adaptation methods,
G. D. Magoulas, M. N. Vrahatis, G. S. Androulakis, · 1999
Earlier work this paper cites.
Least squares support vector machine classifiers,
J. A. Suykens, J. Vandewalle, · 1999
Earlier work this paper cites.
Jnn, a randomized algorithm for training multilayer networks in polynomial time,
A. Elisseeff, H. Paugam-Moisy, · 1999
Earlier work this paper cites.
Applications of artificial neural networks in chemical engineering,
D. M. Himmelblau, · 2000
Earlier work this paper cites.
Artificial neural networks: fundamentals, computing, design, and application,
I. A. Basheer, M. Hajmeer, · 2000
Earlier work this paper cites.
Approximation with artificial neural networks,
B. C. Csáji, · 2001
Earlier work this paper cites.
A review of evidence of health benefit from artificial neural networks in medical intervention,
P. J. Lisboa, · 2002
Earlier work this paper cites.
Radial basis functional link nets and fuzzy reasoning,
C. G. Looney, · 2002
Earlier work this paper cites.
SMOTE: synthetic minority over-sampling technique,
N. V. Chawla, K. W. Bowyer, L. O. Hall, W. P. Kegelmeyer, · 2002
Earlier work this paper cites.
A tutorial on support vector regression,
A. J. Smola, B. Schölkopf, · 2004
Earlier work this paper cites.
A k-nearest neighbor based algorithm for multi-label classification,
M.-L. Zhang, Z.-H. Zhou, · 2005
Earlier work this paper cites.
L. Wang, Support vector machines: theory and applications, volume 177, Springer Science & Business Media, 2005
2005
Earlier work this paper cites.
Statistical comparisons of classifiers over multiple data sets,
J. Demšar, · 2006
Earlier work this paper cites.
Extreme learning machine: theory and applications,
G.-B. Huang, Q.-Y. Zhu, C.-K. Siew, · 2006
Earlier work this paper cites.
On transductive support vector machines,
J. Wang, X. Shen, W. Pan, · 2007
Earlier work this paper cites.
Bivariate empirical mode decomposition,
G. Rilling, P. Flandrin, P. Gonçalves, J. M. Lilly, · 2007
Earlier work this paper cites.
Random features for large-scale kernel machines,
A. Rahimi, B. Recht, · 2007
Earlier work this paper cites.
A fast iterative shrinkage-thresholding algorithm for linear inverse problems,
A. Beck, M. Teboulle, · 2009
Earlier work this paper cites.
Least squares twin support vector machines for pattern classification,
M. A. Kumar, M. Gopal, · 2009
Earlier work this paper cites.
Ensemble empirical mode decomposition: a noise-assisted data analysis method,
Z. Wu, N. E. Huang, · 2009
Earlier work this paper cites.
A sequential minimal optimization algorithm for the all-distances support vector machine,
D. Candel, R. Ñanculef, C. Concha, H. Allende, · 2010
Earlier work this paper cites.
Differential evolution: A survey of the state-of-the-art,
S. Das, P. N. Suganthan, · 2010
Earlier work this paper cites.
A complete ensemble empirical mode decomposition with adaptive noise,
M. E. Torres, M. A. Colominas, G. Schlotthauer, P. Flandrin, · 2011
Earlier work this paper cites.
Analysis of preprocessing vs. cost-sensitive learning for imbalanced classification. open problems on intrinsic data characteristics,
V. López, A. Fernández, J. G. Moreno-Torres, F. Herrera, · 2012
Earlier work this paper cites.
Y. Chauvin, D. E. Rumelhart, Backpropagation: theory, architectures, and applications, Psychology Press, 2013
2013
Earlier work this paper cites.
Empirical wavelet transform,
J. Gilles, · 2013
Earlier work this paper cites.
Variational mode decomposition,
K. Dragomiretskiy, D. Zosso, · 2013
Earlier work this paper cites.
Do we need hundreds of classifiers to solve real world classification problems?,
M. Fernández-Delgado, E. Cernadas, S. Barro, D. Amorim, · 2014
Earlier work this paper cites.
A new weight initialization method for sigmoidal feedforward artificial neural networks,
S. S. Sodhi, P. Chandra, S. Tanwar, · 2014
Earlier work this paper cites.
A survey on feature selection methods,
G. Chandrashekar, F. Sahin, · 2014
Earlier work this paper cites.
Multivariable dynamic modeling for molten iron quality using online sequential random vector functional-link networks with self-feedback connections,
P. Zhou, M. Yuan, H. Wang, Z. Wang, T.-Y. Chai, · 2015
Earlier work this paper cites.
Detecting wind power ramp with random vector functional link (RVFL) network,
Y. Ren, X. Qiu, P. N. Suganthan, G. Amaratunga, · 2015
Earlier work this paper cites.
D. Mishkin, J. Matas, · 2015
Earlier work this paper cites.
A survey of randomized algorithms for training neural networks,
L. Zhang, P. N. Suganthan, · 2016
Earlier work this paper cites.
A comprehensive evaluation of random vector functional link networks,
L. Zhang, P. N. Suganthan, · 2016
Earlier work this paper cites.
Random vector functional link network for short-term electricity load demand forecasting,
Y. Ren, P. N. Suganthan, N. Srikanth, G. Amaratunga, · 2016
Earlier work this paper cites.
Ensemble classification and regression-recent developments, applications and future directions,
Y. Ren, L. Zhang, P. N. Suganthan, · 2016
Earlier work this paper cites.
Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems,
R. Rao, · 2016
Earlier work this paper cites.
Multivariable dynamic modeling for molten iron quality using incremental random vector functional-link networks,
L. Zhang, P. Zhou, M. Yuan, T.-y. Chai, · 2016
Earlier work this paper cites.
A semi-supervised random vector functional-link network based on the transductive framework,
S. Scardapane, D. Comminiello, M. Scarpiniti, A. Uncini, · 2016
Earlier work this paper cites.
Electricity load demand time series forecasting with empirical mode decomposition based random vector functional link network,
X. Qiu, P. N. Suganthan, G. A. Amaratunga, · 2016
Earlier work this paper cites.
Artificial intelligence: The future is superintelligent,
S. Russell, · 2017
Earlier work this paper cites.
Impact of probability distribution selection on RVFL performance,
W. Cao, J. Gao, Z. Ming, S. Cai, H. Zheng, · 2017
Earlier work this paper cites.
Healthy cognitive aging: a hybrid random vector functional-link model for the analysis of Alzheimer’s disease,
P. Dai, F. Gwadry-Sridhar, M. Bauer, M. Borrie, X. Teng, · 2017
Earlier work this paper cites.
Received signal strength based indoor positioning using a random vector functional link network,
W. Cui, L. Zhang, B. Li, J. Guo, W. Meng, H. Wang, L. Xie, · 2017
Earlier work this paper cites.
Robust regularized random vector functional link network and its industrial application,
W. Dai, Q. Chen, F. Chu, X. Ma, T. Chai, · 2017
Earlier work this paper cites.
Data-driven robust RVFLNs modeling of a blast furnace iron-making process using cauchy distribution weighted m-estimation,
P. Zhou, Y. Lv, H. Wang, T. Chai, · 2017
Earlier work this paper cites.
Fuzziness based random vector functional-link network for semi-supervised learning,
W. Cao, J. Gao, Z. Ming, S. Cai, Z. Shan, · 2017
Earlier work this paper cites.
Kernel-based random vector functional-link network for fast learning of spatiotemporal dynamic processes,
K.-K. Xu, H.-X. Li, H.-D. Yang, · 2017
Earlier work this paper cites.
Short-term wind power ramp forecasting with empirical mode decomposition based ensemble learning techniques,
X. Qiu, Y. Ren, P. N. Suganthan, G. A. Amaratunga, · 2017
Earlier work this paper cites.
A randomized algorithm for prediction interval using RVFL networks ensemble,
B. Miskony, D. Wang, · 2017
Earlier work this paper cites.
Benchmarking ensemble classifiers with novel co-trained kernel ridge regression and random vector functional link ensembles [research frontier],
L. Zhang, P. N. Suganthan, · 2017
Earlier work this paper cites.
Visual tracking with convolutional random vector functional link network,
L. Zhang, P. N. Suganthan, · 2017
Earlier work this paper cites.
Self-normalizing neural networks,
G. Klambauer, T. Unterthiner, A. Mayr, S. Hochreiter, · 2017
Earlier work this paper cites.
Initializing convolutional filters with semantic features for text classification,
S. Li, Z. Zhao, T. Liu, R. Hu, X. Du, · 2017
Cited alongside, same era.
Weight initialization of deep neural networks (dnns) using data statistics,
S. Koturwar, S. Merchant, · 2017
Cited alongside, same era.
Deep forest: Towards an alternative to deep neural networks.,
Z.-H. Zhou, J. Feng, · 2017
Cited alongside, same era.
Attention is all you need,
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, I. Polosukhin, · 2017
Cited alongside, same era.
A review on neural networks with random weights,
W. Cao, X. Wang, Z. Ming, J. Gao, · 2018
Cited alongside, same era.
A comprehensive experimental evaluation of orthogonal polynomial expanded random vector functional link neural networks for regression,
Ensemble neural networks with random weights for classification problems,
Y. Liu, W. Cao, Z. Ming, Q. Wang, J. Zhang, Z. Xu, · 2020
Later among the works it cites.
Recent trends in the use of statistical tests for comparing swarm and evolutionary computing algorithms: Practical guidelines and a critical review,
J. Carrasco, S. García, M. Rueda, S. Das, F. Herrera, · 2020
Later among the works it cites.
A new combined model based on multi-objective salp swarm optimization for wind speed forecasting,
Z. Cheng, J. Wang, · 2020
Later among the works it cites.
Enhancing incremental deep learning for FCCU end-point quality prediction,
X. Zhang, Y. Zou, S. Li, · 2020
Later among the works it cites.
Short-term solar power prediction using multi-kernel-based random vector functional link with water cycle algorithm-based parameter optimization,
I. Majumder, P. K. Dash, R. Bisoi, · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
N. Vuković, M. Petrović, Z. Miljković, · 2018
Cited alongside, same era.
Cascaded multi-column RVFL+ classifier for single-modal neuroimaging-based diagnosis of Parkinson’s disease,
J. Shi, Z. Xue, Y. Dai, B. Peng, Y. Dong, Q. Zhang, Y. Zhang, · 2018
Cited alongside, same era.
Enhancing multi-class classification of random forest using random vector functional neural network and oblique decision surfaces,
R. Katuwal, P. N. Suganthan, · 2018
Cited alongside, same era.
Deep learning for computer vision: A brief review,
A. Voulodimos, N. Doulamis, A. Doulamis, E. Protopapadakis, · 2018
Cited alongside, same era.
MobileNetV2: inverted residuals and linear bottlenecks,
M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, L.-C. Chen, · 2018
Cited alongside, same era.
A non-iterative method for pruning hidden neurons in neural networks with random weights,
P. A. Henriquez, G. A. Ruz, · 2018
Cited alongside, same era.
Parsimonious random vector functional link network for data streams,
M. Pratama, P. P. Angelov, E. Lughofer, M. J. Er, · 2018
Cited alongside, same era.
Forecasting crude oil price with multilingual search engine data,
J. Li, L. Tang, S. Wang, · 2020
Later among the works it cites.
RVFL-LQP: RVFL-based link quality prediction of wireless sensor networks in smart grid,
X. Xue, W. Sun, J. Wang, Q. Li, G. Luo, K. Yu, · 2020
Later among the works it cites.
Landslide displacement interval prediction using lower upper bound estimation method with pre-trained random vector functional link network initialization,
C. Lian, Z. Zeng, X. Wang, W. Yao, Y. Su, H. Tang, · 2020
Later among the works it cites.
Fuzzy neural networks and neuro-fuzzy networks: A review the main techniques and applications used in the literature,
P. V. de Campos Souza, · 2020
Later among the works it cites.
Study on artificial intelligence: The state of the art and future prospects,
C. Zhang, Y. Lu, · 2021
Later among the works it cites.
Spatial-temporal fusion graph neural networks for traffic flow forecasting,
M. Li, Z. Zhu, · 2021
Later among the works it cites.
Shapley-lorenz explainable artificial intelligence,
P. Giudici, E. Raffinetti, · 2021
Later among the works it cites.
On the origins of randomization-based feedforward neural networks,
P. N. Suganthan, R. Katuwal, · 2021
Later among the works it cites.
J. Del Ser, D. Casillas-Perez, L. Cornejo-Bueno, L. Prieto-Godino, J. Sanz-Justo, C. Casanova-Mateo, S. Salcedo-Sanz, · 2021
Later among the works it cites.
Deep learning for visual tracking: A comprehensive survey,
S. M. Marvasti-Zadeh, L. Cheng, H. Ghanei-Yakhdan, S. Kasaei, · 2021
Later among the works it cites.
Random vector functional link neural network based ensemble deep learning,
Q. Shi, R. Katuwal, P. N. Suganthan, M. Tanveer, · 2021
Later among the works it cites.
Ensemble of classification models with weighted functional link network,
M. Tanveer, M. A. Ganaie, P. N. Suganthan, · 2021
Later among the works it cites.
Discriminative manifold random vector functional link neural network for rolling bearing fault diagnosis,
X. Li, Y. Yang, N. Hu, Z. Cheng, J. Cheng, · 2021
Later among the works it cites.
Assessing dry weight of hemodialysis patients via sparse Laplacian regularized RVFL neural network with L 2 , 1 {L}_{2,1} -norm,
X. Guo, W. Zhou, Q. Lu, A. Du, Y. Cai, Y. Ding, · 2021
Later among the works it cites.
Deep long short term memory based minimum variance kernel random vector functional link network for epileptic EEG signal classification,
S. Parija, R. Bisoi, P. Dash, M. Sahani, · 2021
Later among the works it cites.
Co-trained random vector functional link network,
M. A. Ganaie, M. Tanveer, P. N. Suganthan, · 2021
Later among the works it cites.
Modified added activation function based exponential robust random vector functional link network with expanded version for nonlinear system identification,
D. Samal, P. K. Dash, R. Bisoi, · 2021
Later among the works it cites.
A turning point prediction method of stock price based on RVFL-GMDH and chaotic time series analysis,
J. Chen, S. Yang, D. Zhang, Y. Nanehkaran, · 2021
Later among the works it cites.
Optimized random vector functional link network to predict oil production from tahe oil field in china,
A. Alalimi, L. Pan, M. A. Al-Qaness, A. A. Ewees, X. Wang, M. Abd Elaziz, · 2021
Later among the works it cites.
A new random vector functional link integrated with mayfly optimization algorithm for performance prediction of solar photovoltaic thermal collector combined with electrolytic hydrogen production system,
M. Abd Elaziz, S. Senthilraja, M. E. Zayed, A. H. Elsheikh, R. R. Mostafa, S. Lu, · 2021
Later among the works it cites.
Prediction of laser cutting parameters for polymethylmethacrylate sheets using random vector functional link network integrated with equilibrium optimizer,
A. H. Elsheikh, T. A. Shehabeldeen, J. Zhou, E. Showaib, M. Abd Elaziz, · 2021
Later among the works it cites.
Machine learning algorithms for improving the prediction of air injection effect on the thermohydraulic performance of shell and tube heat exchanger,
E. M. El-Said, M. Abd Elaziz, A. H. Elsheikh, · 2021
Later among the works it cites.
Adaptive sliding mode control of manipulators based on fuzzy random vector function links for friction compensation,
Z. Zhou, B. Wu, · 2021
Later among the works it cites.
Android malware classification based on random vector functional link and artificial jellyfish search optimizer,
E. T. Elkabbash, R. R. Mostafa, S. I. Barakat, · 2021
Later among the works it cites.
Randomized neural networks for multilabel classification,
V. Chauhan, A. Tiwari, · 2021
Later among the works it cites.
Reinforced fuzzy clustering-based rule model constructed with the aid of exponentially weighted L2 regularization strategy and augmented random vector functional link network,
C. Zhang, S.-K. Oh, W. Pedrycz, Z. Fu, S. Lu, · 2021
Later among the works it cites.
Walk-forward empirical wavelet random vector functional link for time series forecasting,
R. Gao, L. Du, K. F. Yuen, P. N. Suganthan, · 2021
Later among the works it cites.
Predicting the performance of solar dish stirling power plant using a hybrid random vector functional link/chimp optimization model,
M. E. Zayed, J. Zhao, W. Li, A. H. Elsheikh, M. Abd Elaziz, D. Yousri, S. Zhong, Z. Mingxi, · 2021
Later among the works it cites.
Short term solar power forecasting using hybrid minimum variance expanded RVFLN and sine-cosine levy flight PSO algorithm,
D. R. Dash, P. Dash, R. Bisoi, · 2021
Later among the works it cites.
Pinball loss twin support vector clustering,
M. Tanveer, T. Gupta, M. Shah, A. D. N. Initiative, · 2021
Later among the works it cites.
An unsupervised discriminative random vector functional link network for efficient data clustering,
Y. Zhang, Q. Zhu, Y. Peng, W. Kong, · 2021
Later among the works it cites.
Ensemble learning,
Z.-H. Zhou, · 2021
Later among the works it cites.
A novel ensemble method of RVFL for classification problem,
A. K. Malik, M. A. Ganaie, M. Tanveer, P. N. Suganthan, · 2021
Later among the works it cites.
Investigation of diversity strategies in RVFL network ensemble learning for crude oil price forecasting,
L. Yu, Y. Wu, L. Tang, H. Yin, K. K. Lai, · 2021
Later among the works it cites.
Privileged information-driven random network based non-iterative integration model for building energy consumption prediction,
H. Sun, W. Zhai, Y. Wang, L. Yin, F. Zhou, · 2021
Later among the works it cites.
A cerebral microbleed diagnosis method via featurenet and ensembled randomized neural networks,
S.-Y. Lu, D. R. Nayak, S.-H. Wang, Y.-D. Zhang, · 2021
Later among the works it cites.
Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network based ensemble learning,
Y. Hu, B. Qu, J. Wang, J. Liang, Y. Wang, K. Yu, Y. Li, K. Qiao, · 2021
Later among the works it cites.
Review of deep learning: Concepts, cnn architectures, challenges, applications, future directions,
L. Alzubaidi, J. Zhang, A. J. Humaidi, A. Al-Dujaili, Y. Duan, O. Al-Shamma, J. Santamaría, M. A. Fadhel, M. Al-Amidie, L. Farhan, · 2021
Later among the works it cites.
Time series classification using diversified ensemble deep random vector functional link and resnet features,
W. X. Cheng, P. N. Suganthan, R. Katuwal, · 2021
Later among the works it cites.
FAF-DRVFL: Fuzzy activation function based deep random vector functional links network for early diagnosis of alzheimer disease,
R. Sharma, T. Goel, M. Tanveer, S. Dwivedi, R. Murugan, · 2021
Later among the works it cites.
SAR target recognition with modified convolutional random vector functional link network,
Q. Dai, G. Zhang, Z. Fang, B. Xue, · 2021
Later among the works it cites.
Real-time energy management for PV–battery–wind based microgrid using on-line sequential kernel based robust random vector functional link network,
I. Majumder, P. Dash, S. Dhar, · 2021
Later among the works it cites.
Random vector functional link networks for road traffic forecasting: Performance comparison and stability analysis,
E. L. Manibardo, I. Laña, J. Del Ser, · 2021
Later among the works it cites.
Short-term wind speed prediction using hybrid machine learning techniques,
D. Gupta, N. Natarajan, M. Berlin, · 2021
Later among the works it cites.
A new initialization method based on normed statistical spaces in deep networks,
H. Yang, X. Ding, R. Chan, H. Hu, Y. Peng, T. Zeng, · 2021
Later among the works it cites.
Inpatient discharges forecasting for Singapore hospitals by machine learning,
R. Gao, W. X. Cheng, P. Suganthan, K. F. Yuen, · 2022
Closest in time.
Are graph convolutional networks with random weights feasible?,
C. Huang, M. Li, F. Cao, H. Fujita, Z. Li, X. Wu, · 2022
Closest in time.
Ensemble deep learning: A review,
M. A. Ganaie, M. Hu, A. K. Malik, M. Tanveer, P. N. Suganthan, · 2022
Closest in time.
Alzheimer’s disease diagnosis via intuitionistic fuzzy random vector functional link network,
A. K. Malik, M. A. Ganaie, M. Tanveer, P. N. Suganthan, for the Alzheimer’s Disease Neuroimaging Initiative, · 2022
Closest in time.
Minimum variance embedded intuitionistic fuzzy weighted random vector functional link network,
N. Ahmad, M. A. Ganaie, A. K. Malik, K. T. Lai, M. Tanveer, · 2022
Closest in time.
1-norm random vector functional link networks for classification problems,
B. B. Hazarika, D. Gupta, · 2022
Closest in time.
Incremental learning paradigm with privileged information for random vector functional-link networks: IRVFL+,
W. Dai, Y. Ao, L. Zhou, P. Zhou, X. Wang, · 2022
Closest in time.
Artificially intelligent differential diagnosis of enlarged lymph nodes with random vector functional link network plus,
W. Jiao, S. Song, H. Han, W. Wang, Q. Zhang, · 2022
Closest in time.
An auto-weighting incremental random vector functional link network for eeg-based driving fatigue detection,
Y. Zhang, R. Guo, Y. Peng, W. Kong, F. Nie, B.-L. Lu, · 2022
Closest in time.
Online label distribution learning using random vector functional-link network,
J. Huang, C.-M. Vong, W. Qian, Q. Huang, Y. Zhou, · 2022
Closest in time.
Minimum variance embedded random vector functional link network with privileged information,
M. Ganaie, M. Tanveer, A. Malik, P. N. Suganthan, · 2022
Closest in time.
Extended features based random vector functional link network for classification problem,
A. K. Malik, M. A. Ganaie, M. Tanveer, P. N. Suganthan, · 2022
Closest in time.
Conv-eRVFL: Convolutional neural network based ensemble RVFL classifier for alzheimer’s disease diagnosis,
R. Sharma, T. Goel, M. Tanveer, P. N. Suganthan, I. Razzak, R. Murugan, · 2022
Closest in time.
Random vector functional link neural network based ensemble deep learning for short-term load forecasting,
R. Gao, L. Du, P. N. Suganthan, Q. Zhou, K. F. Yuen, · 2022
Closest in time.
Weighting and pruning based ensemble deep random vector functional link network for tabular data classification,
Q. Shi, M. Hu, P. N. Suganthan, R. Katuwal, · 2022
Closest in time.
Ensemble deep random vector functional link neural network for regression,
M. Hu, J. H. Chion, P. N. Suganthan, R. K. Katuwal, · 2022
Closest in time.
Time series forecasting using online performance-based ensemble deep random vector functional link neural network,
L. Du, R. Gao, P. N. Suganthan, D. Z. Wang, · 2022
Closest in time.
Selective ensemble deep bidirectional rvfln for landslide displacement prediction,
X. Yu, C. Lian, Y. Su, B. Xu, X. Wang, W. Yao, H. Tang, · 2022
Closest in time.
Automated layer-wise solution for ensemble deep randomized feed-forward neural network,
M. Hu, R. Gao, P. N. Suganthan, M. Tanveer, · 2022
Closest in time.
Jointly optimized ensemble deep random vector functional link network for semi-supervised classification,
Q. Shi, P. N. Suganthan, J. Del Ser, · 2022
Closest in time.
Ensemble deep random vector functional link network using privileged information for Alzheimer’s disease diagnosis,
M. A. Ganaie, M. Tanveer, · 2022
Closest in time.
Graph embedded ensemble deep randomized network for diagnosis of alzheimer’s disease,
A. K. Malik, M. Tanveer, · 2022
Closest in time.
Deep reservoir computing based random vector functional link for non-sequential classification,
M. Hu, R. Gao, P. Suganthan, · 2022
Closest in time.
Deep randomized feed-forward networks based prediction of human joint angles using wearable inertial measurement unit: Performance comparison,
S. Yang, R. Gao, L. Li, W. T. Ang, · 2022
Closest in time.
Representation learning using deep random vector functional link networks for clustering,
M. Hu, P. N. Suganthan, · 2022
Closest in time.
Randomization-based machine learning in renewable energy prediction problems: critical literature review, new results and perspectives,
J. Del Ser, D. Casillas-Perez, L. Cornejo-Bueno, L. Prieto-Godino, J. Sanz-Justo, C. Casanova-Mateo, S. Salcedo-Sanz, · 2022
Closest in time.
A review on weight initialization strategies for neural networks,
M. V. Narkhede, P. P. Bartakke, M. S. Sutaone, · 2022
Closest in time.
Cluster-based input weight initialization for echo state networks,
P. Steiner, A. Jalalvand, P. Birkholz, · 2022
Closest in time.
Bayesian optimization based dynamic ensemble for time series forecasting,
D. Liang, G. Ruobin, P. N. Suganthan, D. Z. Wang, · 2022
Closest in time.
Dynamic ensemble deep echo state network for significant wave height forecasting,
R. Gao, R. Li, M. Hu, P. N. Suganthan, K. F. Yuen, · 2023
Closest in time.
Random vector functional link forests and extreme learning forests applied to uav automatic target recognition,
V. H. A. Ribeiro, R. Santana, G. Reynoso-Meza, · 2023
Closest in time.
Fabric wrinkle rating model based on resnet18 and optimized random vector functional-link network,
Z. Zhou, Z. Ma, Y. Wang, Z. Zhu, · 2023
Closest in time.
Bayesian random vector functional-link networks for robust data modeling,
S. Scardapane, D. Wang, A. Uncini, · 2059
Closest in time.