Simple classifiers from data dependent hypothesis classes Metadata

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  • Main Title Simple classifiers from data dependent hypothesis classes


  • Author: Cannon, A. (Adam)
    Creator Type: Personal
  • Author: Howse, J. W. (James W.)
    Creator Type: Personal
  • Author: Hush, D. R. (Donald R.)
    Creator Type: Personal
  • Author: Scovel, James C.
    Creator Type: Personal


  • Sponsor: United States. Department of Energy.
    Contributor Type: Organization


  • Name: Los Alamos National Laboratory
    Place of Publication: United States


  • Creation: 2003-01-01


  • English


  • Content Description: In this paper we introduce simple classifiers as an example of how to use the data dependent hypothesis class framework described by Cannon et al. (2002) to explore the performance/computation trade-off in the classifier design problem. We provide a specific example of a simple classifier and demonstrate that it has many remarkable properties: For example it possesses computationally efficient learning algorithms with favorable bounds on estimation error, admits kernel mappings, and is particularly well suited to boosting. We present experimental results on synthetic and real data that suggest that this classifier is competitive with powerful alternative methods.
  • Physical Description: 6 p.


  • Keyword: Classification
  • Keyword: Design
  • Keyword: Kernels
  • Keyword: Hypothesis
  • STI Subject Categories: 97 Mathematical Methods And Computing
  • Keyword: Learning
  • Keyword: Algorithms


  • Conference: Submitted to: ICML Conference, August 2003, Washington, D.C.


  • Name: Office of Scientific & Technical Information Technical Reports
    Code: OSTI


  • Name: UNT Libraries Government Documents Department
    Code: UNTGD

Resource Type

  • Article


  • Text


  • Report No.: LA-UR-03-1810
  • Grant Number: none
  • Office of Scientific & Technical Information Report Number: 976569
  • Archival Resource Key: ark:/67531/metadc932925