Sign-representation of Boolean functions using a small number of monomials

DOI10.1016/j.neunet.2009.03.016zbMath1335.92009OpenAlexW2039615442WikidataQ48627920 ScholiaQ48627920MaRDI QIDQ280399

Erhan Oztop

Publication date: 10 May 2016

Published in: Neural Networks (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.neunet.2009.03.016

zbMATH Keywords

Boolean functionbinary classificationhigher-order neuronpolynomial threshold densitypolynomial threshold unitsign-representation

Mathematics Subject Classification ID

Related Items (3)

Combined weight and density bounds on the polynomial threshold function representation of Boolean functions ⋮ The upper bound of the minimal number of hidden neurons for the parity problem in binary neural networks ⋮ Minimal Sign Representation of Boolean Functions: Algorithms and Exact Results for Low Dimensions

Cites Work

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• Deductive learning
• An Upper Bound on the Minimum Number of Monomials Required to Separate Dichotomies of {−1, 1}ⁿ
• Harmonic Analysis of Polynomial Threshold Functions
• A theory of the learnable
• On the Capabilities of Higher-Order Neurons: A Radial Basis Function Approach

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