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Author Riordan, P. url 
  Title (up) Unsupervised recognition of individual tigers and snow leopards from their footprints Type Miscellaneous
  Year 1998 Publication Animal Conservation Abbreviated Journal  
  Volume 1 Issue Pages 253-262  
  Keywords captive; panthera tigris; panthera uncia; snow leopard; techniques; tiger  
  Abstract This study presents the testing of two unsupervised classification methods for their ability to accurately identify unknown individual tigers, Panthera tigris, and snow leopards, Panthera uncia, from their footprints. A neural-network based method, the Kohonen self-organizing map (SOM), and a Bayesian method, AutoClass, were assessed using hind footprints taken from captive animals under standardized conditions. AutoClass successfully discriminated individuals of both species from their footprints. Classification accuracy was greatest for tigers, with more misclassification of individuals occurring for snow leopards. Examination of variable influence on class formations failed to identify consistently influential measurements for either species. The self-organizing map did not provide accurate classification of individuals for either species. Results were not substantially improved by altering map dimensions nor by using principal components derived from the original data. The interpretation of resulting classifications and the importance of using such techniques in the study of wild animal populations are discussed. The need for further testing in the field is highlighted.  
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  Area Expedition Conference  
  Notes Approved no  
  Call Number SLN @ rana @ 896 Serial 823  
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