DocumentCode
2417933
Title
Branch and bound algorithm for the Bayes classifier
Author
Sze, L. ; Leung, C.H.
Author_Institution
Dept. of Electr. & Electron. Eng., Hong Kong Univ., Hong Kong
Volume
2
fYear
1996
fDate
25-29 Aug 1996
Firstpage
705
Abstract
Given the feature vector from an unknown class, the branch and bound algorithm (BAB) is very efficient for finding the nearest neighbor among the set of reference vectors. The Euclidean distance measure is adopted. In this article, the BAB algorithm is extended so that it can be used with the Bayes classifier which uses the probability measure instead of the Euclidean distance for classification. Gaussian statistics is assumed in the derivations. Satisfactory results are obtained in recognition experiments
Keywords
Bayes methods; Gaussian distribution; pattern classification; probability; tree searching; Bayes classifier; Euclidean distance measure; Gaussian statistics; branch and bound algorithm; feature vector; probability measure; recognition experiments; Character recognition; Current measurement; Euclidean distance; Nearest neighbor searches; Pattern recognition; Probability; Statistics; Tree data structures;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location
Vienna
ISSN
1051-4651
Print_ISBN
0-8186-7282-X
Type
conf
DOI
10.1109/ICPR.1996.546914
Filename
546914
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