NEURAL NETWORK IMPLEMENTATION OF BINARY TREES | ||
| International Conference on Aerospace Sciences and Aviation Technology | ||
| Article 11, Volume 6, ASAT CONFERENCE 2 — 4 May 1995, CAIRO, May 1995, Pages 131-139 PDF (2.4 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/asat.1995.25572 | ||
| Authors | ||
| Ismail A. Farag; Fawzy Ibrahim | ||
| Dr., Department of Specialized Electrical Engineering, Military Technical College Cairo, Egypt. | ||
| 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. | ||
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