Note on a Class of Statistical Recognition Functions

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.