Certainty factors versus Parzen windows as reliability measures in RBF networks
A method is described for using Radial Basis Function (RBF) neural networks to generate a certainty factor reliability measure along with the network's normal output. The certainty factor approach is then compared with another technique for measuring RBF reliability, Parzen windows. Both methods are implemented into RBF networks, and the results of using each approach are compared. Advantages and disadvantages of each approach are discussed. Results indicate that certainty factors are easy to compute and good reliability measure.
certainty factors parzen windows reliability measures rbf neural networks pattern recognition