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Author (up) Fernandez, C.; Valle, C.; Saravia, F.; Allende, H. pdf  doi
  Title Behavior analysis of neural network ensemble algorithm on a virtual machine cluster Type
  Year 2012 Publication Neural Computing & Applications Abbreviated Journal Neural Comput. Appl.  
  Volume 21 Issue 3 Pages 535-542  
  Keywords Ensemble learning; Artificial neural networks; Virtualization; Multicore processor; Parallel algorithms  
  Abstract Ensemble learning has gained considerable attention in different learning tasks including regression, classification, and clustering problems. One of the drawbacks of the ensemble is the high computational cost of training stages. Resampling local negative correlation (RLNC) is a technique that combines two well-known methods to generate ensemble diversity-resampling and error negative correlation-and a fine-grain parallel approach that allows us to achieve a satisfactory balance between accuracy and efficiency. In this paper, we introduce a structure of the virtual machine aimed to test diverse selection strategies of parameters in neural ensemble designs, such as RLNC. We assess the parallel performance of this approach on a virtual machine cluster based on the full virtualization paradigm, using speedup and efficiency as performance metrics, for different numbers of processors and training data sizes.  
  Address [Fernandez, Cesar; Valle, Carlos; Saravia, Francisco; Allende, Hector] Univ Tecn Federico Santa Maria, Dept Comp Sci, Valparaiso 110 V, Chile, Email:;  
  Corporate Author Thesis  
  Publisher Springer Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0941-0643 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000301578900014 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 251  
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