• DocumentCode
    1750057
  • Title

    Ensemble classification of the VF dataset with limited merging

  • Author

    Yang, Zhihong ; Greenshieids, I.R.

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Connecticut Univ., Storrs, CT, USA
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    117
  • Lastpage
    122
  • Abstract
    Ensemble classification (the concurrent development of K independent classifications) is a common practice in Gibbs classification. In this paper, we describe an adaptation of R. Azencott´s (1992) theorem on finite-time annealing, coupled with a limited merging protocol, which produces a final low-energy Gibbs classification of the Virtual Female (VF) data set
  • Keywords
    free energy; image classification; medical image processing; merging; simulated annealing; Virtual Female data set; concurrent development; ensemble classification; finite-time annealing; independent classifications; limited merging protocol; low-energy Gibbs classification; medical images; Annealing; Bayesian methods; Computer science; Data engineering; History; Image processing; Merging; Pattern recognition; Probability distribution; Protocols;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer-Based Medical Systems, 2001. CBMS 2001. Proceedings. 14th IEEE Symposium on
  • Conference_Location
    Bethesda, MD
  • ISSN
    1063-7125
  • Print_ISBN
    0-7695-1004-3
  • Type

    conf

  • DOI
    10.1109/CBMS.2001.941707
  • Filename
    941707
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1750057