RANDOM GRAPHS IN A NEURAL COMPUTATION MODEL
Author:
Alexandros Gerbessiotis a
| Affiliation: | a Computer Science Department, New Jersey Institute of Technology, Newark, NJ 07102, USA. |
DOI:
10.1080/0020716031000079518
Publication Frequency:
15 issues per year
Published in:
International Journal of Computer Mathematics,
Volume
80,
Issue
6
June
2003
, pages 689
- 707
Subjects:
Analysis - Mathematics;
Bioinformatics;
Computer Mathematics;
Discrete Mathematics;
Mathematical Finance;
Mathematical Logic;
Mathematical Numerical Analysis;
Systems & Computer Architecture;
Number of References: 12
Formats available:
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(English)
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Abstract
We examine in this work the following graph theory problem that arises in neural computations that involve the learning of boolean expressions by studying the asymptotic connectivity properties of $G_
n\comma 1/\lpar kn\rpar ^ 1/2![]() $ random graphs, where k is a fixed positive integer. For an undirected graph $G = \lpar V\comma \; E\rpar $ let $N\lpar X\comma \; Y\rpar = \lcub v \in V - \lpar X \cup Y\rpar \!\mid$ $ \exists x \in X\ \hbox with \ \lpar v\comma \; x\rpar \in E\rcub $ . For fixed k construct an undirected graph $G = \lpar V\comma \; E\rpar $ such that for all disjoint sets $A\comma \; B \subseteq V$ such that $\vert A \vert = \vert B \vert = k$ , and $C = N\lpar A\comma \; B\rpar \cap N\lpar B\comma \; A\rpar $ , set C is such that $\vert C \vert$ is either exactly k or as close to k as possible. Asymptotic results for large values of k are also presented.
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| Keywords: Random Graphs; Connectivity Properties; Neural Networks |
| view references (12) |

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n\comma 1/\lpar kn\rpar ^
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