1995 | OriginalPaper | Buchkapitel
From Prime Implicants to Modular Feedforward Networks
verfasst von : Uwe Hartmann
Erschienen in: Artificial Neural Nets and Genetic Algorithms
Verlag: Springer Vienna
Enthalten in: Professional Book Archive
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The paper utilises prime implicants and minimal polynomials in order to reduce the size of the training set of a neural feedforward network. We propose a heuristic in order to compute reduced polynomials which are often able to reduce the training set since the computation of minimal polynomials is intractable. Further abstractions lead to modular feedforward sub-architectures of neural networks for special training patterns. Finally, we introduce overlapping modular sub-architectures for distinct training patterns.