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      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1996 Working Papers
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      •   Research Commons
      • University of Waikato Research
      • Computing and Mathematical Sciences
      • Computer Science Working Paper Series
      • 1996 Working Papers
      • View Item
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      Understanding what machine learning produces - Part I: Representations and their comprehensibility

      Cunningham, Sally Jo; Humphrey, Matthew C.; Witten, Ian H.
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      Cunningham, S. J., Humphrey, M. C. & Witten, I. H. (1996). Understanding what machine learning produces - Part I: Representations and their comprehensibility. (Working paper 96/21). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
      Permanent Research Commons link: https://hdl.handle.net/10289/1181
      Abstract
      The aim of many machine learning users is to comprehend the structures that are inferred from a dataset, and such users may be far more interested in understanding the structure of their data than in predicting the outcome of new test data. Part I of this paper surveys representations based on decision trees, production rules and decision graphs that have been developed and used for machine learning. These representations have differing degrees of expressive power, and particular attention is paid to their comprehensibility for non-specialist users. The graphic form in which a structure is portrayed also has a strong effect on comprehensibility, and Part II of this paper develops knowledge visualization techniques that are particularly appropriate to help answer the questions that machine learning users typically ask about the structures produced.
      Date
      1996-10
      Type
      Working Paper
      Series
      Computer Science Working Papers
      Report No.
      96/21
      Collections
      • 1996 Working Papers [32]
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