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Self-growing neural network architecture using crisp and fuzzy entropyThe 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 problen of telling two spirals apart are shown and discussed.
Document ID
19930042653
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Cios, Krzysztof J.
(NASA Lewis Research Center; Ohio Aerospace Inst. Cleveland, United States)
Date Acquired
August 16, 2013
Publication Date
January 1, 1992
Publication Information
Publisher: Society of Photo-Optical Instrumentation Engineers (SPIE Proceedings. Vol. 1710, pt. 2)
Subject Category
Cybernetics
Accession Number
93A26650
Funding Number(s)
CONTRACT_GRANT: NSF DDM-90-15333
Distribution Limits
Public
Copyright
Other

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