Probabilistic decision trees and multilayered perceptrons

Summary form only given, as follows. The authors proposed an algorithm to compute a multilayered perceptron for classification problems, based on the design of a binary decision tree. They showed how to modify this algorithm for using ternary logic, introducing a `don´t know´ class. This modification could be applied to any heuristic based on recursive construction of a decision tree. Another way of dealing with uncertainty for improving generalization performance is to construct probabilistic decision trees. The authors explained how to modify the preceding heuristics for constructing such trees and associating probabilistic multilayered perceptrons