A False Acceptance Error Controlling Method for Hyperspherical Classifiers

Authors

Chen-Wen Yen, National Sun Yat-Sen UniversityFollow
Chieh-Neng Young, National Sun Yat-Sen UniversityFollow
Mark L. Nagurka, Marquette UniversityFollow

Document Type

Article

Language

eng

Publication Date

3-2004

Publisher

Elsevier

Source Publication

Neurocomputing

Source ISSN

0925-2312

Abstract

Controlling false acceptance errors is of critical importance in many pattern recognition applications, including signature and speaker verification problems. Toward this goal, this paper presents two post-processing methods to improve the performance of hyperspherical classifiers in rejecting patterns from unknown classes. The first method uses a self-organizational approach to design minimum radius hyperspheres, reducing the redundancy of the class region defined by the hyperspherical classifiers. The second method removes additional redundant class regions from the hyperspheres by using a clustering technique to generate a number of smaller hyperspheres. Simulation and experimental results demonstrate that by removing redundant regions these two post-processing methods can reduce the false acceptance error without significantly increasing the false rejection error.

Comments

Accepted version. Neurocomputing, Vol. 57 (March 2004): 295-312. DOI. © 2003 Elsevier B.V. Used with permission.

Recommended Citation

Yen, Chen-Wen; Young, Chieh-Neng; and Nagurka, Mark L., "A False Acceptance Error Controlling Method for Hyperspherical Classifiers" (2004). Mechanical Engineering Faculty Research and Publications. 151.
https://epublications.marquette.edu/mechengin_fac/151