NDLI: Enhancing performance of Bayes classifier for the hardened password mechanism

Content Provider	IEEE Xplore Digital Library
Author	Pavaday, N. Soyjaudah, K.M.S.
Copyright Year	2007
Description	Author affiliation: Univ. of Purdue West Lafayette, Indiana (Pavaday, N.) \|\| Univ. of Mauritius, Moka (Soyjaudah, K.M.S.)
Abstract	In pattern recognition, Bayes classifier is considered a powerful tool in the decision making process as its gives the lowest probability of committing a classification error. This has been highlighted in a number of previous works. The diversity in algorithm parameters, criteria used and number of users involved as well as the evaluation method makes the task of comparing their results and selecting an appropriate system a very thorny one. The purpose of this paper is four fold: to study the different approaches reported using a true password, to establish how normalization and using a single variance/mean impact on the results, using a maximum number of features does necessarily improve performance and finally demonstrate that performance optimization is feasible through careful selection of features and approach taken.
Starting Page	1
Ending Page	7
File Size	796074
Page Count	7
File Format	PDF
ISBN	9781424409860
DOI	10.1109/AFRCON.2007.4401574
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-09-26
Publisher Place	South Africa
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biometrics Authentication Hardware Industrial training Bayesian methods Pattern recognition Decision making Optimization Security Covariance matrix covariance matrix Biometric Bayes Classifier
Content Type	Text
Resource Type	Article

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