Improved Classification In Flat Networks

dc.contributor.author	Gannapal, Madhu	en_US
dc.date.accessioned	2010-07-19T19:55:06Z
dc.date.available	2010-07-19T19:55:06Z
dc.date.issued	2010-07-19
dc.date.submitted	January 2010	en_US
dc.identifier.other	DISS-10692	en_US
dc.identifier.uri	http://hdl.handle.net/10106/4954
dc.description.abstract	It is shown that optimal flat networks can be found as solutions to least squares problems. An algorithm is presented to improve existing classifier training methods by changing the desired outputs. The algorithm is based on minimum probability of error. The algorithm's performance is compared with those of other algorithms including the Bayes Gaussian classifier. The Convergence of training and the effects of outliers are analyzed in all the algorithms presented here.	en_US
dc.description.sponsorship	Manry, Michael T.	en_US
dc.language.iso	EN	en_US
dc.publisher	Electrical Engineering	en_US
dc.title	Improved Classification In Flat Networks	en_US
dc.type	M.Engr.	en_US
dc.contributor.committeeChair	Manry, Michael T.	en_US
dc.degree.department	Electrical Engineering	en_US
dc.degree.discipline	Electrical Engineering	en_US
dc.degree.grantor	University of Texas at Arlington	en_US
dc.degree.level	masters	en_US
dc.degree.name	M.Engr.	en_US
dc.identifier.externalLink	https://www.uta.edu/ra/real/editprofile.php?onlyview=1&pid=281
dc.identifier.externalLinkDescription	Link to Research Profiles