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Characterization of Linearly Separable Boolean Functions: A Graph-Theoretic Perspective
- Source :
- IEEE Transactions on Neural Networks and Learning Systems. 28:1542-1549
- Publication Year :
- 2017
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2017.
-
Abstract
- In this paper, we present a novel approach for studying Boolean function in a graph-theoretic perspective. In particular, we first transform a Boolean function $f$ of $n$ variables into an induced subgraph $H_{f}$ of the $n$ -dimensional hypercube, and then, we show the properties of linearly separable Boolean functions on the basis of the analysis of the structure of $H_{f}$ . We define a new class of graphs, called hyperstar, and prove that the induced subgraph $H_{f}$ of any linearly separable Boolean function $f$ is a hyperstar. The proposal of hyperstar helps us uncover a number of fundamental properties of linearly separable Boolean functions in this paper.
- Subjects :
- Discrete mathematics
021103 operations research
Computer Networks and Communications
Parity function
Two-element Boolean algebra
0211 other engineering and technologies
02 engineering and technology
Boolean algebras canonically defined
Complete Boolean algebra
Computer Science Applications
Combinatorics
Boolean domain
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Boolean expression
Circuit minimization for Boolean functions
Boolean function
Software
Mathematics
Subjects
Details
- ISSN :
- 21622388 and 2162237X
- Volume :
- 28
- Database :
- OpenAIRE
- Journal :
- IEEE Transactions on Neural Networks and Learning Systems
- Accession number :
- edsair.doi.dedup.....1b898d7ce116cfedecdcb997a87c0e3b
- Full Text :
- https://doi.org/10.1109/tnnls.2016.2542205