Communications of the ACM
Parallel analog neural networks for tree searching
AIP Conference Proceedings 151 on Neural Networks for Computing
Learning decision trees from random examples needed for learning
Information and Computation
Learning 2u DNF formulas and ku decision trees
COLT '91 Proceedings of the fourth annual workshop on Computational learning theory
Pattern recognition: statistical, structural and neural approaches
Pattern recognition: statistical, structural and neural approaches
A technique for upper bounding the spectral norm with applications to learning
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Learning decision trees using the Fourier spectrum
SIAM Journal on Computing
Proper learning algorithm for functions of k terms under smooth distributions
COLT '95 Proceedings of the eighth annual conference on Computational learning theory
Machine Learning
Investigation and Reduction of Discretization Variance in Decision Tree Induction
ECML '00 Proceedings of the 11th European Conference on Machine Learning
A complete fuzzy decision tree technique
Fuzzy Sets and Systems - Theme: Learning and modeling
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Fuzzy decision trees have been substantiated to be a valuable tool and more efficient than neural networks for pattern recognition task due to some facts like computation in making decisions are simpler and important features can be selected automatically during the design process. Here we present a feed forward neural network which learns fuzzy decision trees during the descent along the branches for its classification. Every decision instances of decision tree are represented by a node in neural network. The neural network provides the degree of membership of each possible move to the fuzzy set ≪ good move ≫ corresponding to each decision instance. These fuzzy values constitute the core of the probability of selecting the move out of the set of the children of the current node. This results in a natural way for driving the sharp discrete-state process running along the decision tree by means of incremental methods on the continuous-valued parameters of the neural network. A simulation program in C has been deliberated and developed for analyzing the consequences. The effectiveness of the learning process is tested through experiments with three real-world classification problems.