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Neural Processing Letters

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07-03-2022

Classification and Enumeration of Linearly Separable Boolean Functions Based on Optimal Separation System

Authors: Wei Jin, Fangyue Chen, Qinbin He

Published in: Neural Processing Letters | Issue 5/2022

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Abstract

In this paper, some profound mathematical properties of linearly separable Boolean functions (LSBF) are revealed based on the optimal separation system derived from support vector machine, and a novel approach is proposed for the classification and enumeration of this kind of function. Completely different the existing linear program method, the method presented now can be used not only to enumerate the number of self-dual function classes and the total number of LSBFs, but also to calculate the optimal separation system of any balanced LSBF accurately.

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Title: Classification and Enumeration of Linearly Separable Boolean Functions Based on Optimal Separation System
Authors: Wei Jin
Fangyue Chen
Qinbin He
Publication date: 07-03-2022
Publisher: Springer US
Published in: Neural Processing Letters / Issue 5/2022
Print ISSN: 1370-4621
Electronic ISSN: 1573-773X
DOI: https://doi.org/10.1007/s11063-022-10781-1

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