The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - January (1969 vol.18)
pp: 76-79
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.
INDEX TERMS
Binary measurement space, decision theory, dichotomy problem, expected decision error, Lagrangian multiplier, least-square error approximation, recognition function, Walsh function.
CITATION
T. Ito, "Note on a Class of Statistical Recognition Functions", IEEE Transactions on Computers, vol.18, no. 1, pp. 76-79, January 1969, doi:10.1109/T-C.1969.222530
34 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool