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Neural Network Classification and Formalization
Type of publication: Journal paper
Citation: Fiesler-94.2
Journal: Computer Standards & Interfaces
Volume: 16
Number: 03
Year: 1994
Month: 6
ISSN: 0920-5489
Abstract: In order to assist the field of neural networks in maturing, a formalization and a solid foundation are essential. Additionally, to permit the introduction of formal proofs, it is essential to have an all- encompassing formal mathematical definition of a neural network. This publication offers a neural network formalization consisting of a topological taxonomy, a uniform nomenclature, and an accompanying consistent mnemonic notation. Supported by this formalization, both a flexible hierarchical and a universal mathematical definition are presented.
Userfields: special={Neural Network Standards}, language={English}, remark={Available on-line.}, ipdmembership={neuron learning},
Keywords: architecture, definition, formalization, mnemonic notation, neural network classification, neural network determination, nomenclature, standardization, terminology, topological taxonomy, topology
Projects Idiap
Authors Fiesler, Emile
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Total mark: 0