Minimum Variance Embedded Random Vector Functional Link Network with Privileged Information
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[1] Muhammad Tanveer,et al. LSTSVM classifier with enhanced features from pre-trained functional link network , 2020, Appl. Soft Comput..
[2] P. N. Suganthan,et al. A comprehensive evaluation of random vector functional link networks , 2016, Inf. Sci..
[3] Halbert White,et al. Chapter 9 Approximate Nonlinear Forecasting Methods , 2006 .
[4] Dejan J. Sobajic,et al. Learning and generalization characteristics of the random vector Functional-link net , 1994, Neurocomputing.
[5] Lawrence D. Jackel,et al. Handwritten Digit Recognition with a Back-Propagation Network , 1989, NIPS.
[6] Guang-Bin Huang,et al. Extreme Learning Machine for Multilayer Perceptron , 2016, IEEE Transactions on Neural Networks and Learning Systems.
[7] Yoh-Han Pao,et al. Stochastic choice of basis functions in adaptive function approximation and the functional-link net , 1995, IEEE Trans. Neural Networks.
[8] M. A. Ganaie,et al. Improved Sparse Pinball Twin SVM , 2019, 2019 IEEE International Conference on Systems, Man and Cybernetics (SMC).
[9] Gonzalo A. Ruz,et al. A non-iterative method for pruning hidden neurons in neural networks with random weights , 2018, Appl. Soft Comput..
[10] Najdan Vukovic,et al. A comprehensive experimental evaluation of orthogonal polynomial expanded random vector functional link neural networks for regression , 2017, Appl. Soft Comput..
[11] Isabelle Guyon,et al. Neural Network Recognizer for Hand-Written Zip Code Digits , 1988, NIPS.
[12] P. N. Suganthan,et al. Regularized robust fuzzy least squares twin support vector machine for class imbalance learning , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).
[13] Jooyoung Park,et al. Universal Approximation Using Radial-Basis-Function Networks , 1991, Neural Computation.
[14] Bernard Widrow,et al. The No-Prop algorithm: A new learning algorithm for multilayer neural networks , 2013, Neural Networks.
[15] Robert P. W. Duin,et al. Feedforward neural networks with random weights , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol.II. Conference B: Pattern Recognition Methodology and Systems.
[16] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[17] Hongming Zhou,et al. Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).
[18] Y. Takefuji,et al. Functional-link net computing: theory, system architecture, and functionalities , 1992, Computer.
[19] Feilong Cao,et al. A study on effectiveness of extreme learning machine , 2011, Neurocomputing.
[20] Bijaya K. Panigrahi,et al. Indian summer monsoon rainfall prediction: A comparison of iterative and non-iterative approaches , 2017, Appl. Soft Comput..
[21] Allan Pinkus,et al. Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function , 1991, Neural Networks.
[22] Umesh V. Vazirani,et al. An Introduction to Computational Learning Theory , 1994 .
[23] Chunxia Zhang,et al. Generalized extreme learning machine autoencoder and a new deep neural network , 2017, Neurocomputing.
[24] Alexandros Iosifidis,et al. Minimum Class Variance Extreme Learning Machine for Human Action Recognition , 2013, IEEE Transactions on Circuits and Systems for Video Technology.
[25] Halbert White,et al. Approximate Nonlinear Forecasting Methods , 2006 .
[26] Hubert A.B. Te Braake,et al. Random activation weight neural net (RAWN) for fast non-iterative training. , 1995 .
[27] Junying Hu,et al. A Deep Neural Network Based on ELM for Semi-supervised Learning of Image Classification , 2017, Neural Processing Letters.
[28] Ling Tang,et al. A non-iterative decomposition-ensemble learning paradigm using RVFL network for crude oil price forecasting , 2017, Appl. Soft Comput..
[29] Sarika Jalan,et al. Identification of Chimera using Machine Learning , 2020, Chaos.
[30] Ponnuthurai Nagaratnam Suganthan,et al. Oblique Decision Tree Ensemble via Twin Bounded SVM , 2020, Expert Syst. Appl..
[31] Senén Barro,et al. Do we need hundreds of classifiers to solve real world classification problems? , 2014, J. Mach. Learn. Res..
[32] Alexandros Iosifidis,et al. Minimum Variance Extreme Learning Machine for human action recognition , 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
[33] Kurt Hornik,et al. Multilayer feedforward networks are universal approximators , 1989, Neural Networks.
[34] Leonardo Ramos Rodrigues,et al. Building selective ensembles of Randomization Based Neural Networks with the successive projections algorithm , 2017, Appl. Soft Comput..