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Author (up) Ramirez, F.; Allende, H. pdf  doi
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  Title Detection of flaws in aluminium castings: a comparative study between generative and discriminant approaches Type
  Year 2013 Publication Insight Abbreviated Journal Insight  
  Volume 55 Issue 7 Pages 366-371  
  Keywords Anomaly detection; SVM; SVDD  
  Abstract Automatic anomaly detection has become a key issue in many engineering applications due to the increasing amount of data in need of analysis. Addressing this kind of task using pattern recognition methods requires a proper design of the learning strategy, given the reduced amount of flawed cases available for training compared to that of normal instances, which has been shown to hinder the performance of traditional classification algorithms. Moreover, positive examples are often costly and hard to collect, which may prevent the use of traditional discriminant approaches such as artificial neural networks. In this paper, we compare two well-known generative and discriminant pattern recognition algorithms in the task of flaw detection in aluminium castings and show that defects can be accurately identified without prior knowledge of positive cases, using only information of regular structures, achieving a geometric mean of over 0.9.  
  Address [Ramirez, F.] Univ Tecn Federico Santa Maria, Dept Informat, Valparaiso, Chile  
  Corporate Author Thesis  
  Publisher British Inst Non-Destructive Testing Place of Publication Editor  
  Language English Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 1354-2575 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000322558600007 Approved  
  Call Number UAI @ eduardo.moreno @ Serial 297  
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