References

(As it is difficult to compile a full list of publications on ELM theories and applications, here we only show the references on hand. The work on the compilation is undergoing and the completed list will be given once  it is done)

S. M. Shahrear Tanzil, William Hoiles, and Vikram Krishnamurthy, “Adaptive Scheme for Caching YouTube Content in
a Cellular Network: Machine Learning Approach,”IEEE Open Access, vol. 5, pp. 5870-5881, 2017.

J. Deng, S. Fruhholz, Z. ZHANG, and B. Schuller, “Recognizing Emotions From Whispered Speech
Based on Acoustic Feature Transfer Learning,”IEEE Open Access, vol. 2, pp. 5235-5246, 2017.

Yimin Yang and Q.M.Jonathan Wu, Autoencoder with invertible functions for dimension reduction and image reconstruction," IEEE Transactions on Systems, Man and Cybernetics: Systems, 2017

L. L. C. Kasun, Y. Yang, G.-B. Huang, and Z. Zhang, “Dimension Reduction With Extreme Learning Machine,”IEEE Transactions on Image Processing, vol. 25, no. 8, pp. 3906-3918, 2016.

Yimin Yang and Q.M.Jonathan Wu, "Multilayer extreme learning machine with subnetwork nodes for representation learning," IEEE Transactions on Cybernetics, vol. 46, no. 11, pp. 2570-2583, 2016.

Z. Huang, Y. Yu, J. Gu, and H. Liu, “An Efficient Method for Traffic Sign Recognition Based on Extreme Learning Machine,”IEEE Transactions on Cybernetics,  vol. 47. no. 4, pp. 920-933, 2016.

Yimin Yang, and et al, "Data partition learning with multiple extreme learning machines, IEEE Transactions on Cybernetics, Vol. 45, pp. 1463-1475. 2015. 

G.-B. Huang, “What are Extreme Learning Machines? Filling the Gap between Frank Rosenblatt's Dream and John von Neumann's Puzzle,”Cognitive Computation, vol. 7, pp. 263-278, 2015.

G.-B. Huang, Z. Bai, L. L. C. Kasun, and C. M. Vong, “Local Receptive Fields Based Extreme Learning Machine,”IEEE Computational Intelligence Magazine, vol. 10, no. 2, pp. 18-29, 2015.

Y. Chen, E. Yao, and A. Basu, "A 128 channel Extreme Learning Machine based Neural Decoder for Brain Machine Interfaces," IEEE Transactions on Biomedical Circuits and Systems, vol. 10, no. 3, pp. 679-692, 2016

Y. Wang, H. Yu, L. Ni, G.-B. Huang, M. Yan, C. Weng, W. Yang and J. Zhao, "An Energy-efficient Nonvolatile In-memory Computing Architecture for Extreme Learning Machine by Domain-wall Nanowire Devices," IEEE Transactions on Nanotechnologies, vol. 14, no. 6, pp. 998-1012, 2015

M. Suri and V. Parmar, "Exploiting Intrinsic Variability of Filamentary Resistive Memory for Extreme Learning Machine Architectures," IEEE Transactions on Nanotechnologies, vol. 14, no. 6, pp. 963-968, 2015

A. Akusok, K. Bjork, Y. Miche, and A. Lendasse, "High-Performance Extreme Learning Machines: A Complete Toolbox for Big Data Applications," IEEE Open Access, vol. 3, 2015

Z. Bai, L. L. C. Kasun, and G.-B. Huang, “Generic Object Recognition with Local Receptive Fields
Based Extreme Learning Machine,”2015 INNS Conference on Big Data, San Francisco, August 8-10, 2015.

Anton Akusok, Yoan Miche, Juha Karhunen, Kaj-Mikael Bjork, Rui Nian, and Amaury Lendasse, “Arbitrary Category Classification of Websites Based on Image Content,”IEEE Computational Intelligence Magazine, vol. 10, no. 2, pp. 30-41, 2015.

Jiexiong Tang, Chenwei Deng, and Guang-Bin Huang, “Extreme Learning Machine for Multilayer Perceptron,”IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 4, pp. 809-821, 2016.

G. Huang, G.-B. Huang, S. Song, and K. You, “Trends in Extreme Learning Machines: A Review,”Neural Netwokrs, vol. 61, no. 1, pp. 32-48, 2015.

G.-B. Huang, “An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels,”Cognitive Computation, vol. 6, pp. 376-390, 2014. (also briefing the differences and relationships between differnent methods such as SVM, LS-SVM, RVFL, QuickNet, Rosenblatt's Perceptron, etc)

L. L. C. Kasun, H. Zhou, G.-B. Huang, and C. M. Vong, “Representational Learning with Extreme Learning Machine for Big Data,” IEEE Intelligent Systems,  vol. 28, no. 6, pp. 31-34, December 2013. (This paper shows ELM auto-encoder outperforms various state-of-art deep learning methods in MNIST OCR dataset.)

J. Tang, C. Deng, G.-B. Huang, and B. Zhao, "Compressed-Domain Ship Detection on Spaceborne Optical Image Using Deep Neural Network and Extreme Learning Machine," IEEE Transactions on Geoscience and Remote Sensing, vol. 53, no. 3, pp. 1174-1185, 2015

G.-B. Huang, M.-B. Li, L. Chen and C.-K. Siew, “Incremental Extreme Learning Machine With Fully Complex Hidden Nodes,” Neurocomputing, vol. 71, pp. 576-583, 2008. (also briefing the differences between RVFL and RBF network)

G. Huang, S. Song, J. N. D. Gupta, and C. Wu, “Semi-supervised and Unsupervised Extreme Learning Machines,” (in press) IEEE Transactions on Cybernetics, vol. 44, no. 12, pp. 2405-2417, 2014.

G.-B. Huang, H. Zhou, X. Ding, and R. Zhang, “Extreme Learning Machine for Regression and Multiclass Classification,” IEEE Transactions on Systems, Man, and Cybernetics - Part B: Cybernetics,  vol. 42, no. 2, pp. 513-529, 2012. (This paper shows that ELM generally outperforms SVM/LS-SVM in various kinds of cases.)

Z. Bai, G.-B. Huang, D. Wang, H. Wang and M. B. Westover, "Sparse Extreme Learning Machine for Classification," IEEE Transactions on Cybernetics, vol. 44, no. 10, pp. 1858-1870, 2014.

G.-B. Huang, X. Ding, and H. Zhou, “Optimization Method Based Extreme Learning Machine for Classification”, Neurocomputing, vol. 74, pp. 155-163, 2010

G.-B. Huang, L. Chen and C.-K. Siew, “Universal Approximation Using Incremental Constructive Feedforward Networks with Random Hidden Nodes”, IEEE Transactions on Neural Networks, vol. 17, no. 4, pp. 879-892, 2006.

N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “A Fast and Accurate On-line Sequential Learning Algorithm for Feedforward Networks”, IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411-1423, 2006.

G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme Learning Machine: A New Learning Scheme of Feedforward Neural Networks,” 2004 International Joint Conference on Neural Networks (IJCNN'2004), (Budapest, Hungary), July 25-29, 2004.

G.-B. Huang, Q.-Y. Zhu and C.-K. Siew, “Extreme Learning Machine: Theory and Applications”, Neurocomputing, vol. 70, pp. 489-501, 2006.

G. Feng, G.-B. Huang, Q. Lin, and R. Gay, “Error Minimized Extreme Learning Machine with Growth of Hidden Nodes and Incremental Learning”, IEEE Transactions on Neural Networks, vol. 20, no. 8, pp. 1352-1357, 2009.

M.-B. Li, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “Fully Complex Extreme Learning Machine,” Neurocomputing, vol. 68, pp. 306-314, 2005.

G.-B. Huang and L. Chen, “Convex Incremental Extreme Learning Machine,” Neurocomputing, vol. 70, pp. 3056-3062, 2007. (available for fuzzy inference system, etc)

G.-B. Huang and L. Chen, “Enhanced Random Search Based Incremental Extreme Learning Machine,” Neurocomputing, vol. 71, pp. 3460-3468, 2008. (available for fuzzy inference system, etc), (higher prediction accuracy, fast learning rate and compact network achieved)

G.-B. Huang, Q.-Y. Zhu, K. Z. Mao, C.-K. Siew, P. Saratchandran, and N. Sundararajan, “Can Threshold Networks Be Trained Directly?” IEEE Transactions on Circuits and Systems-II, vol. 53, no. 3, pp. 187-191, 2006.