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| Title: | A comparison study of mahalanobis-taguchi system and neural network for multivariate pattern recognition |
| Author (s): | Hong, Jungeui Cudney, Elizabeth Anne Taguchi, Genichi Jugulum, Rajesh Paryani, Kioumars Ragsdell, Kenneth M. |
| Department/Lab Affiliations: | Design Engineering Center Engineering Management & Systems Engineering |
| Keywords: | Mahalanobis-Taguchi System neural networks |
| Issue Date: | 2005-11 |
| Publisher: | American Society of Mechanical Engineers ASME |
| Citation: | Hong, Jungeui, Elizabeth A. Cudney, Genichi Taguchi, Rajesh Jugulum, Kioumars Paryani, and Kenneth M. Ragsdell. A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition. 2005 ASME International Mechanical Engineering Congress and Exposition, November 2005: IMECE2005-80029. |
| 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. |
| Type: | Article - Conference proceedings text |
| In Title: | 2005 ASME International Mechanical Engineering Congress and Exposition |
| Copyright Notice: | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. Pre-print: author cannot archive; Post-print: author cannot archive; FULL COPYRIGHT INFORMATION: |
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| title | A comparison study of mahalanobis-taguchi system and neural network for multivariate pattern recognition |
| contributor.author | Hong, Jungeui |
| contributor.author | Cudney, Elizabeth Anne |
| contributor.author | Taguchi, Genichi |
| contributor.author | Jugulum, Rajesh |
| contributor.author | Paryani, Kioumars |
| contributor.author | Ragsdell, Kenneth M. |
| contributor.deptlab | Design Engineering Center |
| contributor.deptlab | Engineering Management & Systems Engineering |
| subject | Mahalanobis-Taguchi System |
| subject | neural networks |
| date.issued | 2005-11 |
| publisher | American Society of Mechanical Engineers ASME |
| identifier.citation | Hong, Jungeui, Elizabeth A. Cudney, Genichi Taguchi, Rajesh Jugulum, Kioumars Paryani, and Kenneth M. Ragsdell. A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition. 2005 ASME International Mechanical Engineering Congress and Exposition, November 2005: IMECE2005-80029. |
| identifier.pub.URI | |
| description.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. |
| type | Article - Conference proceedings |
| type.DCMIType | text |
| relation.isPartOf | 2005 ASME International Mechanical Engineering Congress and Exposition |
| rights | This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. |
| rights | Pre-print: author cannot archive; Post-print: author cannot archive; |
| rights.URI | |
| rights.URI | |
| identifier.persist.URI | |
| date.available | 2009-01-16T16:48:36Z |