Learning algorithms for stack filter classifiers

PDF Version Also Available for Download.

Description

Stack Filters define a large class of increasing filter that is used widely in image and signal processing. The motivations for using an increasing filter instead of an unconstrained filter have been described as: (1) fast and efficient implementation, (2) the relationship to mathematical morphology and (3) more precise estimation with finite sample data. This last motivation is related to methods developed in machine learning and the relationship was explored in an earlier paper. In this paper we investigate this relationship by applying Stack Filters directly to classification problems. This provides a new perspective on how monotonicity constraints can help ... continued below

Creation Information

Porter, Reid B; Hush, Don & Zimmer, Beate G January 1, 2009.

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.

Authors

Publisher

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

Stack Filters define a large class of increasing filter that is used widely in image and signal processing. The motivations for using an increasing filter instead of an unconstrained filter have been described as: (1) fast and efficient implementation, (2) the relationship to mathematical morphology and (3) more precise estimation with finite sample data. This last motivation is related to methods developed in machine learning and the relationship was explored in an earlier paper. In this paper we investigate this relationship by applying Stack Filters directly to classification problems. This provides a new perspective on how monotonicity constraints can help control estimation and approximation errors, and also suggests several new learning algorithms for Boolean function classifiers when they are applied to real-valued inputs.

Source

  • International Symposium on Mathematical Morphology ; August 24, 2009 ; Groningen, The Netherlands

Language

Item Type

Identifier

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

  • Report No.: LA-UR-09-01010
  • Report No.: LA-UR-09-1010
  • Grant Number: AC52-06NA25396
  • Office of Scientific & Technical Information Report Number: 956436
  • Archival Resource Key: ark:/67531/metadc927939

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, 2009

Added to The UNT Digital Library

  • Nov. 13, 2016, 7:26 p.m.

Description Last Updated

  • Dec. 12, 2016, 12:17 p.m.

Usage Statistics

When was this article last used?

Yesterday: 0
Past 30 days: 0
Total Uses: 6

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

Porter, Reid B; Hush, Don & Zimmer, Beate G. Learning algorithms for stack filter classifiers, article, January 1, 2009; [New Mexico]. (digital.library.unt.edu/ark:/67531/metadc927939/: accessed September 21, 2018), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.