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.;
