Extreme Learning Machines 2013: Algorithms and Applications by Fuchen Sun, Kar-Ann Toh, Manuel Grana Romay, Kezhi Mao

By Fuchen Sun, Kar-Ann Toh, Manuel Grana Romay, Kezhi Mao

In contemporary years, ELM has emerged as a innovative means of computational intelligence, and has attracted significant attentions. An severe studying computer (ELM) is a unmarried layer feed-forward neural community alike studying procedure, whose connections from the enter layer to the hidden layer are randomly generated, whereas the connections from the hidden layer to the output layer are realized via linear studying equipment. the phenomenal benefits of maximum studying computing device (ELM) are its quickly studying velocity, trivial human interfere and excessive scalability.

This e-book comprises a few chosen papers from the foreign convention on severe studying desktop 2013, which used to be held in Beijing China, October 15-17, 2013. This convention goals to assemble the researchers and practitioners of maximum studying computer from a number of fields together with man made intelligence, biomedical engineering and bioinformatics, procedure modelling and keep an eye on, and sign and photograph processing, to advertise examine and discussions of “learning with out iterative tuning".

This booklet covers algorithms and purposes of ELM. It offers readers a look of the most recent advancements of ELM.

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34, 17–24 (2003) 25. S. Suresh, K. Dong, H. Kim, A sequential learning algorithm for self-adaptive resource allocation network classifier. Neurocomputing 7(3), 3012–3019 (2010) 26. L. Kuntcheva, Combining Pattern Classifiers-Methods and Algorithms (Wiley, New York, 2004) An Improved Weight Optimization and Cholesky Decomposition Based Regularized Extreme Learning Machine for Gene Expression Data Classification ShaSha Wei, HuiJuan Lu, Yi Lu and MingYi Wang Abstract The gene expression data classification problem has been widely studied due to the development of DNA microarray technology.

Kuntcheva, Combining Pattern Classifiers-Methods and Algorithms (Wiley, New York, 2004) An Improved Weight Optimization and Cholesky Decomposition Based Regularized Extreme Learning Machine for Gene Expression Data Classification ShaSha Wei, HuiJuan Lu, Yi Lu and MingYi Wang Abstract The gene expression data classification problem has been widely studied due to the development of DNA microarray technology. However, how to classify the complex gene expression data accurately still remains as a major problem.

Zou, F. Xu, Sequential learning radial basis function network for real-time tidal level predictions. Ocean Eng. 57, 49–55 (2013) 14. J. Platt, A resource allocating network for function interpolation. Neural Comput. 3(2), 213–225 (1991) 15. -W. Lu, N. Sundararajan, P. Saratchandran, A sequential learning scheme for function approximation using minimal radial basis function neural networks. Neural Comput. 9, 461–478 (1997) 16. -B. -Y. -K. Siew, Extreme learning machine: theory and applications. Neurocomputing 70, 489–501 (2006) 17.

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