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Author Tulyakov, S. ♦ Chaohong Wu ♦ Govindaraju, V.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2007
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Special computer methods ♦ Natural sciences & mathematics ♦ Life sciences; biology ♦ Biochemistry
Subject Keyword Biometrics ♦ Chaos ♦ Handwriting recognition ♦ Impedance matching ♦ Pattern classification ♦ Venus ♦ Iterative algorithms ♦ Iterative methods ♦ Biosensors ♦ Testing
Abstract Traditional classifier combination algorithms use either non-trainable combination functions or functions trained with the goal of better separation of genuine and impostor class matching scores. Both of these approaches are suboptimal if the system is intended to perform identification of the input among few enrolled classes or templates. In this work we propose training combination functions with the goal of minimizing the misclassification rate. The main idea of proposed methods is to use a set of best or strong impostors, and attempt to construct a classifier combination function separating genuine and best impostor matching scores. We have to use iterative methods for such training, since the set of best impostors depends on currently used combination function. We present two iterative methods for constructing combination functions and perform experiments on handwritten word recognizers and biometric matchers.
Description Author affiliation: Univ. at Buffalo, Buffalo (Tulyakov, S.; Chaohong Wu; Govindaraju, V.)
ISBN 9781424415960
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2007-09-27
Publisher Place USA
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Size (in Bytes) 408.87 kB
Page Count 5
Starting Page 1
Ending Page 5

Source: IEEE Xplore Digital Library