• DocumentCode
    1195098
  • Title

    Using background knowledge to improve inductive learning: a case study in molecular biology

  • Author

    Hirsh, Haym ; Noordewier, Michiel

  • Author_Institution
    Dept. of Comput. Sci., Rutgers Univ., Piscataway, NJ, USA
  • Volume
    9
  • Issue
    5
  • fYear
    1994
  • Firstpage
    3
  • Lastpage
    6
  • Abstract
    This work uses background knowledge to reexpress training data in a form more appropriate for inductive learning. The approach dramatically improves the results of decision-tree and neural network learning methods.<>
  • Keywords
    DNA; learning by example; molecular biophysics; neural nets; background knowledge; case study; decision-tree; inductive learning; learning; molecular biology; neural network; training data; Computer aided software engineering; DNA; Encoding; Laboratories; Microorganisms; Sampling methods; Sequences; Signal processing; Speech; Training data;
  • fLanguage
    English
  • Journal_Title
    IEEE Expert
  • Publisher
    ieee
  • ISSN
    0885-9000
  • Type

    jour

  • DOI
    10.1109/64.331477
  • Filename
    331477
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1195098