By Fuchen Sun, Kar-Ann Toh, Manuel Grana Romay, Kezhi Mao
In contemporary years, ELM has emerged as a progressive means of computational intelligence, and has attracted significant attentions. An severe studying laptop (ELM) is a unmarried layer feed-forward neural community alike studying approach, 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 exceptional advantages of maximum studying laptop (ELM) are its quick studying velocity, trivial human interfere and excessive scalability.
This ebook comprises a few chosen papers from the overseas convention on severe studying desktop 2013, which used to be held in Beijing China, October 15-17, 2013. This convention goals to collect the researchers and practitioners of maximum studying computer from a number of fields together with man made intelligence, biomedical engineering and bioinformatics, method modelling and keep an eye on, and sign and photograph processing, to advertise study and discussions of “learning with out iterative tuning".
This publication covers algorithms and functions of ELM. It offers readers a look of the most recent advancements of ELM.
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Extra info for Extreme Learning Machines 2013: Algorithms and Applications
Example text
87GHZ CPU. The codes used for ELM, SVR and LS-SVR were downloaded from 1 , 2 , and 3 respectively. In order to extensively verify the performance of ESVR, ELM, SVR and LS-SVR, twelve data sets of different sizes and dimensions were downloaded from UC Irvine Machine Learning Repository 4 or StatLib library 5 for simulation. These data sets can be divided into three categories according to different sizes and feature dimensions. Baskball, Strike, Cloud, and Autoprice are of small size and low dimensions.
Lu Computer Science Department, Prairie View A&M University, Prairie View, TX 77446, USA F. Sun et al. 1007/978-3-319-04741-6_5, © Springer International Publishing Switzerland 2014 55 56 S. Wei et al. microarray hybridization test after pretreatment, which usually represents in the form of matrix [2]. Data classification [3] is used to divide genes into different groups according to the similarity or pattern of gene expression data. In 1999, Alon firstly classified the colon cancer data set into multi-groups using hierarchical clustering algorithm [4].
However, there are inherent limitations in ELM. Studies show [10] that in most cases ELM has high performance, but hidden layer initial parameters (connection weights, the offset value, the number of nodes) of ELM have big impacts on classification accuracy. Huang proposed I-ELM which increases hidden layer node of ELM one by one to improve the convergence rate.