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NeuroCOLT
Technical Report NC-TR-95-026
Probabilistic
Decision Trees
and Multilayered Perceptrons
Pascal
Bigot and Michel Cosnard
LIP, ENS, Lyon
France
Abstract
We propose a new algorithm to compute a multilayered perceptron for
classification problems, based on the design of a binary decision
tree. We show how to modify this algorithm for using ternary logic,
introducing a Don'tKnow class. This modification could be applied
to any heuristic based on the recursive construction of a decision
tree. Another way of dealing with uncertainty for improving generalization
performance is to construct probabilistic decision trees. We explain
how to modify the preceding heuristics for constructing such trees
and associating probabilistic multilayered perceptrons.
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Postscript
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