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Author Chu, J. T. ♦ Chueh, J. C.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©1967
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Abstract Upper bounds for the error probability of a Bayes decision function are derived in terms of the differences among the probability distributions of the features used in character recognition. Applications to feature selection and error reduction are discussed. It is shown that if a sufficient number of well-selected features is used, the error probability can be made arbitrarily small.
ISSN 00045411
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 1967-04-01
Publisher Place New York
e-ISSN 1557735X
Journal Journal of the ACM (JACM)
Volume Number 14
Issue Number 2
Page Count 8
Starting Page 273
Ending Page 280


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Source: ACM Digital Library