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Note on a Class of Statistical Recognition Functions
January 1969 (vol. 18 no. 1)
pp. 76-79
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, Jan. 1969, doi:10.1109/T-C.1969.222530
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