Issue No. 01 - January (1969 vol. 18)

ISSN: 0018-9340

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. , pp. 76-79, January 1969, doi:10.1109/T-C.1969.222530