0

Full Content is available to subscribers

Subscribe/Learn More  >

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

[+] Author Affiliations
Jungeui Hong

Chungju National University

Elizabeth A. Cudney, Kenneth M. Ragsdell

University of Missouri at Rolla

Genichi Taguchi

Ohken Associates

Rajesh Jugulum

Massachusetts Institute of Technology

Kioumars Paryani

General Motors Corporation

Paper No. IMECE2005-80029, pp. 109-115; 7 pages
doi:10.1115/IMECE2005-80029
From:
  • ASME 2005 International Mechanical Engineering Congress and Exposition
  • Design Engineering, Parts A and B
  • Orlando, Florida, USA, November 5 – 11, 2005
  • Conference Sponsors: Design Engineering Division
  • ISBN: 0-7918-4215-0 | eISBN: 0-7918-3769-6
  • Copyright © 2005 by ASME

abstract

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

Copyright © 2005 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In