Simple classifiers from data dependent hypothesis classes

PDF Version Also Available for Download.

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.

Creation Information

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

Context

This article is part of the collection entitled: Office of Scientific & Technical Information Technical Reports and was provided by UNT Libraries Government Documents Department to Digital Library, a digital repository hosted by the UNT Libraries. More information about this article can be viewed below.

Who

People and organizations associated with either the creation of this article or its content.

Provided By

UNT Libraries Government Documents Department

Serving as both a federal and a state depository library, the UNT Libraries Government Documents Department maintains millions of items in a variety of formats. The department is a member of the FDLP Content Partnerships Program and an Affiliated Archive of the National Archives.

Contact Us

What

Descriptive information to help identify this article. Follow the links below to find similar items on the Digital Library.

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.

Language

Item Type

Identifier

Unique identifying numbers for this article in the Digital Library or other systems.

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

Collections

This article is part of the following collection of related materials.

Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

What responsibilities do I have when using this article?

When

Dates and time periods associated with this article.

Creation Date

  • January 1, 2003

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

Description Last Updated

  • Dec. 12, 2016, 4:48 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 4
Total Uses: 9

Interact With This Article

Here are some suggestions for what to do next.

Start Reading

PDF Version Also Available for Download.

International Image Interoperability Framework

IIF Logo

We support the IIIF Presentation API

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 November 16, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.