RT Journal Article
T1 On the probability of error in fuzzy discrimination problems
A1 Pardo Llorente, Julio Ángel
A1 Taneja, I.J.
AB The decision rule which minimizes the probability of error, in the discrimination problem, is the Bayes decision rule which assigns x to the class with the highest a posteriori probability. This rule leads to a partial probability of error which is given by P(e)(x) = 1-max p(C(i)/x) for each x is-an-element-of X. Prior to observing X, the probability of error associated with X is defined as P(e) = E(X)[P(e)(x)]. Tanaka, Okuda and Asai formulated the discrimination problem with fuzzy classes and fuzzy information using the probability of fuzzy events and derived a bound for the average error probability, when the decision in the classifier is made according to the fuzzified Bayes method. The aim is to obtain bounds for the average error probability in terms of (alpha,beta)-information energy, when the decision in the classifier is made according to the fuzzified Bayes method.
PB Emerald Group Publishing Limited
SN 0368-492X
YR 1992
FD 1992
LK https://hdl.handle.net/20.500.14352/57848
UL https://hdl.handle.net/20.500.14352/57848
DS Docta Complutense
RD 7 abr 2025