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Author Slagle, James
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Language English
Subject Keyword Pattern classification ♦ Relaxation algorithm ♦ Linearly separable ♦ Centering ♦ Central hyperplanes ♦ Dead zone ♦ Centrality criteria ♦ Pattern recognition ♦ Accelerated relaxation ♦ Hyperplane ♦ Linear discriminants
Abstract In two-class pattern recognition, it is a standard technique to have an algorithm finding hyperplanes which separates the two classes in a linearly separable training set. The traditional methods find a hyperplane which separates all points in one class from all points in the other, but such a hyperplane is not necessarily centered in the empty space between the two classes. Since a central hyperplane does not favor one class or the other, it should have a lower error rate in classifying new points and is therefore better than a noncentral hyperplane. Six algorithms for finding central hyperplanes are tested on three data sets. Although frequently used in practice, the modified relaxation algorithm is very poor. Three algorithms which are defined in the paper are found to be quite good.
Description Affiliation: Naval Research Lab., Washington, DC (Slagle, James)
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2005-08-01
Publisher Place New York
Journal Communications of the ACM (CACM)
Volume Number 22
Issue Number 3
Page Count 6
Starting Page 178
Ending Page 183


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Source: ACM Digital Library