The MATLAB codes of basic ELM (with randomly generated hidden nodes, random neurons) are available for download now. These random hidden nodes include sigmoid, RBF, Fourier Series, etc.
Sources of ELM with kernels (for both regression and multi-class classification) are also available for download now.
Sources of OS-ELM are available for download.
Thank Vladislavs Dovgalecs from University of Rouen, Italy, for the kind contribution of C/C++ version of ELM, which can be downloaded from this ELM web portal. A blog entry describing briefly the algorithm and its main benefits as well as a link to the code can be found at http://dovgalecs.com/blog/extreme-learning-machine-matlab-mex-implementation/
This code has not been verifitied by this ELM web portal. Researchers can refer to the contributor for any questions/insutrations.
Thank A. Akusok, K. Bjork, Y. Miche, and A. Lendasse for the kind contribution of Python version of ELM can be found in https://pypi.python.org/pypi/hpelm
Thank David Lambert for the kind contribution of Python version of ELM, which can be downloaded from this ELM web portal. A blog entry describing briefly the algorithm and a link to the code can be found at https://github.com/dclambert/Python-ELM
This code has not been verifitied by this ELM web portal. Researchers can refer to the contributor for any questions/insutrations.
Thank Dong Li for the kind contribution of Java version of ELM, which can be downloaded from this ELM web portal.
This code has not been verifitied by this ELM web portal. Researchers can refer to the contributor for any questions/insutrations.
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.
Jiexiong Tang, Chenwei Deng, and Guang-Bin Huang, “Extreme Learning Machine for Multilayer Perceptron,” (accepted by)IEEE Transactions on Neural Networks and Learning Systems, 2015.
Z. Xie, K. Xu, W. Shan, L. Liu, Y. Xiong, and H. Huang, "Projective Feature Learning for 3D Shapes with Multi-View Depth Images," Pacific Graphics, vol. 24, no. 7, 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
C. Savojardo, P. Fariselli, and R. Casadio, “BETAWARE: a machine-learning tool to detect and predict transmembrane beta barrel proteins in Prokaryotes,” Bioinformatics, Jan 13 2013. [source-codes link: BETAWARE] (for protein and genome analysis)
J.-N. Wang, J.-L. Jin, Y. Geng, S.-L. Sun, H.-L. Xu, Y.-H. Lu and Z.-M. Su, "An accurate and efficient method to predict the electronic excitation energies of BODIPY fluorescent dyes," Journal of Computational Chemistry, vol. 34, no. 7, pp. 566-575, 2013 [Free Online Web Service:EEEBPre -ELM based prediction of electronic excitation energies for BODIPY dyes, which is freely accessible to public at the web site: http://202.198.129.218, has been built for prediction by the authors. This web server can return the predicted electronic excitation energy values of BODIPY dyes that are high consistent with the experimental values. The authors hope that this web server would be helpful to theoretical and experimental chemists in related research.]
W. Zong, G.-B. Huang, and Y. Chen, “Weighted extreme learning machine for imbalance learning,” Neurocomputing, vol. 101, pp. 229-242, 2013.
Y. Yang, Y. Wang, and X. Yuan, "Bidirectional extreme learning machine for regression problem and its learning effectiveness," IEEE Transactions on Neural Networks and Learning Systems, Vol. 23, pp. 1498 - 1505, 2012
J. Cao, Z. Lin, and G.-B. Huang, “Self-adaptive evolutionary extreme learning machine,” Neural Processing Letters, vol. 36, pp. 285-305, 2012.
M.-B. Li, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “Fully Complex Extreme Learning Machine,” Neurocomputing, vol. 68, pp. 306-314, 2005.
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. Huang, S. Song, J. N. D. Gupta, and C. Wu, “Semi-supervised and Unsupervised Extreme Learning Machines,” (in press) IEEE Transactions on Cybernetics, 2014.