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Author (up) Allende, H.; Salas, R.; Moraga, C. pdf  doi
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  Title A robust and effective learning algorithm for feedforward neural networks based on the influence function Type Journal Article
  Year 2003 Publication Lecture Notes in Computer Sciences Abbreviated Journal Lect. Notes Comput. Sc.  
  Volume 2652 Issue Pages 28-36  
  Keywords feedforward artificial neural networks; robust learning; effective parameter estimate  
  Abstract The learning process of the Feedforward Artificial Neural Networks relies on the data, though a robustness analysis of the parameter estimates of the model must be done due to the presence of outlying observations in the data. In this paper we seek the robust properties in the parameter estimates in the sense that the influence of aberrant observations or outliers in the estimate is bounded so the neural network is able to model the bulk of data. We also seek a trade off between robustness and efficiency under a Gaussian model. An adaptive learning procedure that seeks both aspects is developed. Finally we show some simulations results applied to the RESEX time series.  
  Address Univ Tecn Federico Santa Maria, Dept Informat, Valparaiso, Chile, Email: hallende@inf.utfsm.cl  
  Corporate Author Thesis  
  Publisher Springer-Verlag Berlin Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0302-9743 ISBN Medium  
  Area Expedition Conference Pattern Recognition And Image Analysis  
  Notes WOS:000184832300004 Approved no  
  Call Number UAI @ eduardo.moreno @ Serial 35  
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