NeuroCOLT

Neural Networks and Computational Learning Theory

 

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NeuroCOLT Technical Report NC-TR-95-007

VC-Dimensions for Graphs

Evangelos Kranakis
Carleton University,

Danny Krizanc
Carleton University,

Berthold Ruf
Technical University Graz

Jorge Urrutia
University of Ottawa

Gerhard J. Woeginger
Technical University Graz

Abstract
We study set systems over the vertex set (or edge set) of some graph that are induced by special graph properties like clique, connectedness, path, star, tree, etc. We derive a variety of combinatorial and computational results on the $\vc$ (Vapnik-Chervonenkis) dimension of these set systems. For most of these set systems (e.g.\ for the systems induced by trees, connected sets, or paths), computing the $\vc$-dimension is an $\np$-hard problem. Moreover, determining the $\vc$-dimension for set systems induced by neighborhoods of single vertices is complete for the class $\lognp$. In contrast to these intractability results, we show that the $\vc$-dimension for set systems induced by stars is computable in polynomial time. For set systems induced by paths or cycles, we determine the extremal graphs $G$ with the minimum number of edges such that $\vc_{{\cal P}}(G)\ge k$. Finally, we show a close relation between the $\vc$-dimension of set systems induced by connected sets of vertices and the $\vc$ dimension of set systems induced by connected sets of edges; the argument is done via the line graph of the corresponding graph.

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