Proceedings Paper
Self-growing neural network architecture using crisp and fuzzy entropy| Format | Member Price | Non-Member Price |
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Paper Abstract
The paper briefly describes the self-growing neural network algorithm, CID3, which makes decision trees equivalent to hidden layers of a neural network. The algorithm generates a feedforward architecture using crisp and fuzzy entropy measures. The results for a real-life recognition problem of distinguishing defects in a glass ribbon, and for a benchmark problem of telling two spirals apart are shown and discussed.
Paper Details
Date Published: 1 July 1992
PDF: 14 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140154
Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)
PDF: 14 pages
Proc. SPIE 1710, Science of Artificial Neural Networks, (1 July 1992); doi: 10.1117/12.140154
Show Author Affiliations
Krzysztof J. Cios, NASA Lewis Research Ctr. and Ohio Aerospace Institute (United States)
Published in SPIE Proceedings Vol. 1710:
Science of Artificial Neural Networks
Dennis W. Ruck, Editor(s)
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