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- W2911740681 abstract "Multiple layer artificial neural network (ANN) structure is capable of implementing arbitrary input-output mappings. Similarly, hierarchical classifiers, more commonly known as decision trees, possess the capabilities of generating arbitrarily complex decision boundaries in an n-dimensional space. Given a decision tree, it is possible to restructure it as a multilayered neural network. The objective of this paper is to show how this mapping of decision trees into multilayer neural network structure can be exploited for the systematic design of a class of layered neural networks, called entropy nets, that have far fewer connections." @default.
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- W2911740681 date "1995-05-01" @default.
- W2911740681 modified "2023-09-27" @default.
- W2911740681 title "NEURAL NETWORK IMPLEMENTATION OF BINARY TREES" @default.
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- W2911740681 doi "https://doi.org/10.21608/asat.1995.25572" @default.
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