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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)

G.­B. Huang, “What are Extreme Learning Machines? Filling the Gap between Frank Ronblatt's Dream and John von

Neumann's Puzzle,” (in press) Cognitive Computation, April 2015.

G.­B. Huang, Z. Bai, L. L. C. Kasun, and C. M. Vong, “Local Receptive Fields Bad Extreme Learning Machine,”IEEE

Computational Intelligence Magazine, vol. 10, no. 2, pp. 18­29, 2015.

Anton Akusok, Yoan Miche, Juha Karhunen, Kaj­Mikael Bjork, Rui Nian, and Amaury Lendas, “Arbitrary Category

Classification of Websites Bad on Image Content,”IEEE Computational Intelligence Magazine, vol. 10, no. 2, pp. 30­

41, 2015.

Jiexiong Tang, Chenwei Deng, and Guang­Bin Huang, “Extreme Learning Machine for Multilayer Perceptron,”

(accepted by)IEEE Transactions on Neural Networks and Learning Systems, 2015.

G. Huang, G.­B. Huang, S. Song, and K. You, “Trends in Extreme Learning Machines: A Review,”Neural Netwokrs, vol.

61, no. 1, pp. 32­48, 2015.

G.­B. Huang, “An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels,”Cognitive

Computation, vol. 6, pp. 376­390, 2014. (also briefing the differences and relationships between differnent methods

such as SVM, LS­SVM, RVFL, QuickNet, Ronblatt's Perceptron, etc)

L. L. C. Kasun, H. Zhou, G.­B. Huang, and C. M. Vong, “Reprentational Learning with Extreme Learning Machine for

Big Data,” IEEE Intelligent Systems, vol. 28, no. 6, pp. 31­34, December 2013. (This paper shows ELM auto­encoder

outperforms various state­of­art deep learning methods in MNIST OCR datat.)

J. Tang, C. Deng, G.­B. Huang, and B. Zhao, "Compresd­Domain Ship Detection on Spaceborne Optical Image Using

Deep Neural Network and Extreme Learning Machine," IEEE Transactions on Geoscience and Remote Sensing, 2014

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. 576­583, 2008. (also briefing the differences between RVFL and RBF network)

G. Huang, S. Song, J. N. D. Gupta, and C. Wu, “Semi­supervid and Unsupervid Extreme Learning Machines,” (in

press) IEEE Transactions on Cybernetics, 2014. (Also comparing with deep learning/deep autoencoder)

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. 513­529, 2012. (This

paper shows that ELM generally outperforms SVM/LS­SVM in various kinds of cas.)

Z. Bai, G.­B. Huang, D. Wang, H. Wang and M. B. Westover, "Spar Extreme Learning Machine for Classification," (in

press) IEEE Transactions on Cybernetics, 2014.

G.­B. Huang, X. Ding, and H. Zhou, “Optimization Method Bad Extreme Learning Machine for Classification”,

Neurocomputing, vol. 74, pp. 155­163, 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. 879­892, 2006. (Technical

Report ICIS/46/2003) (Manuscript submitted on Oct 29, 2003, revid on May 8, 2005)

N.­Y. Liang, G.­B. Huang, P. Saratchandran, and N. Sundararajan, “A Fast and Accurate On­line Sequential Learning

Algorithm for Feedforward Networks”, IEEE Transactions on Neural Networks, vol. 17, no. 6, pp. 1411­1423, 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 25­29,

2004.

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

pp. 489­501, 2006.

H.­J. Rong, G.­B. Huang, P. Saratchandran, and N. Sundararajan, “On­Line Sequential Fuzzy Extreme Learning

Machine for Function Approximation and Classification Problems”, IEEE Transactions on Systems, Man, and

Cybernetics: Part B, vol. 39, no. 4, pp. 1067­1072, 2009.

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. 1352­1357, 2009.

Y. Lan, Y. C. Soh, and G.­B. Huang, “Enmble of Online Sequential Extreme Learning Machine,” Neurocomputing, vol.

72, pp. 3391­3395, 2009.

M.­B. Li, G.­B. Huang, P. Saratchandran, and N. Sundararajan, “Fully Complex Extreme Learning Machine,”

Neurocomputing, vol. 68, pp. 306­314, 2005.

G.­B. Huang and L. Chen, “Convex Incremental Extreme Learning Machine,” Neurocomputing, vol. 70, pp. 3056­3062,

2007. (available for fuzzy inference system, etc)

G.­B. Huang and L. Chen, “Enhanced Random Search Bad Incremental Extreme Learning Machine,”

Neurocomputing, vol. 71, pp. 3460­3468, 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 Systems­II, vol. 53, no. 3, pp. 187­191, 2006.

C.­W. T. Yeu , M.­H. Lim, G.­B. Huang, A. Agarwal, and Y. S. Ong, “A New Machine Learning Paradigm for Terrain

Reconstruction”, IEEE Geoscience and Remote Sensing Letters, vol. 3, no. 3, pp. 382­386, 2006.

R. Zhang, G.­B. Huang, N. Sundararajan, and P. Saratchandran, “Multi­Category Classification Using Extreme Learning

Highlights

If you wish to recommend good papers

to highlight, plea contact us

ELM+CNN

Yujun Zeng, Xin Xu, Yuqiang Fang,

Kun Zhao, “Traffic Sign Recognition

Using Extreme Learning

Classifier with Deep Convolutional

Features,” The 2015 International

Conference on Intelligence Science

and Big Data Engineering (IScIDE

2015),Suzhou, June 14­16, 2015.

Clustering

G. Huang, S. Song, J. N. D. Gupta, and

C. Wu, “Semi­supervid and

Unsupervid Extreme Learning

Machines,” (in press) IEEE

Transactions on Cybernetics, 2014.

IEEE Intelligent Systems (ELM

Special Issue)

Cambria, et al, “Extreme Learning

Machines,” IEEE Transactions on

Cybernetics, vol. 28, no. 6, pp. 30­59,

2013.

Algorithms

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.

Security Asssment

Y. Xu, et al, "A Reliable Intelligent

System for Real­Time Dynamic

Security Asssment of Power

Systems," IEEE Transactions on

Power Systems, vol. 27, no. 3, pp.

1235­1263, 2012

Data Privacy

S. Samet and A. Miri, "Privacy­

prerving back­propagation and

extreme learning machine algorithms,"

Data & Knowledge Engineering, vol.

79­80, pp. 40­61, 2012

EEG and Seizure Detection

Y. Song, J. Crowcrofta, and J. Zhang,

"Automatic epileptic izure detection

in EEGs bad on optimized sample

entropy and extreme learning

machine," Journal of Neuroscience

Methods, pp. 132­146, 2012

Image Quality Asssment

S. Decherchi, et al, "Circular­ELM for

the reduced­reference asssment of

perceived image quality,"

Neurocomputing, 2012

Image Super Resolution

L. An and B. Bhanu, "Image Super­

Resolution By Extreme Learning

Machine," 2012 IEEE International

Conference on Image Processing,

September 30 ­ October 3, 2012,

Orlando, Florida, USA (better than

conventional kernel method bad

and compressive nsing bad

techniques)

Source codesOpen problemsConferencesMaterialsReferences

Rearch positions

Machine for Microarray Gene Expression Cancer Diagnosis”, IEEE/ACM Transactions on Computational Biology and

Bioinformatics, vol. 4, no. 3, pp. 485­495, 2007

FPGA

S. Decherchi, et al, "Efficient Digital

Implementation of Extreme Learning

Machines for Classification," IEEE

Transactions on Circuits and Systems

­ II, vol. 59, no. 8, pp. 496­500, 2012

Face Recognition

Y. Choi, et al, "Incremental face

recognition for large­scale social

network rvices," Pattern

Recognition, vol. 45, no. 8, pp. 2868­

2883, 2012

Human Action Recognition

R. Minhas, et al, "Incremental Learning

in Human Action Recognition Bad

on Snippets," IEEE Transactions on

Circuits and Systems for Video

Technology, vol. 22, no. 11, pp. 1529­

1541, 2012

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