Simple classifiers from data dependent hypothesis classes

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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.

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6 p.

Creation Information

Cannon, A. (Adam); Howse, J. W. (James W.); Hush, D. R. (Donald R.) & Scovel, James C. January 1, 2003.

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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.

Source

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

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

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  • January 1, 2003

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

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  • Dec. 12, 2016, 4:48 p.m.

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Cannon, A. (Adam); Howse, J. W. (James W.); Hush, D. R. (Donald R.) & Scovel, James C. Simple classifiers from data dependent hypothesis classes, article, January 1, 2003; United States. (digital.library.unt.edu/ark:/67531/metadc932925/: accessed September 24, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.