DocumentCode
1100939
Title
Note on a Class of Statistical Recognition Functions
Author
Ito, Takayasu
Issue
1
fYear
1969
Firstpage
76
Lastpage
79
Abstract
Statistical recognition procedures can be derived from the functional form of underlying probability distributions. Successive approximation to the probability function leads to a class of recognition procedures. In this note we give a hierarchical method of designing recognition functions which satisfy both the least-square error property and a minimum decision error rate property, although our discussions are restricted to a binary measurement space and its dichotomous classification.
Keywords
Binary measurement space, decision theory, dichotomy problem, expected decision error, Lagrangian multiplier, least-square error approximation, recognition function, Walsh function.; Decision theory; Design methodology; Error analysis; Extraterrestrial measurements; Hierarchical systems; Indium tin oxide; Lagrangian functions; Pattern recognition; Probability distribution; Statistical analysis; Binary measurement space, decision theory, dichotomy problem, expected decision error, Lagrangian multiplier, least-square error approximation, recognition function, Walsh function.;
fLanguage
English
Journal_Title
Computers, IEEE Transactions on
Publisher
ieee
ISSN
0018-9340
Type
jour
DOI
10.1109/T-C.1969.222530
Filename
1671123
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