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Sobik, F. and Sommerfeld, E. (1982). A graph theoretic approach for representation and classification of structured objects. In Proceedings of the 5.th European Conference on Artificial Intelligence, ECAI.

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This paper is cited in the following contexts:
Learning Relational Concepts with Decision Trees - Geibel, Wysotzki (1996)   (4 citations)  (Correct)

....structures, the main problem is to construct suitable attributes for describing the graphs or nodes in the training set automatically. Such attributes must capture the elementary attributes of the nodes as well as structural properties of the considered graph. One possibility examined in [ Sobik and Sommerfeld, 1982 ] is the usage of subgraphs as attributes. When testing for an attribute one has to check for subgraph isomorphism. The main problem with this approach is to determine the subgraphs to be used as attributes and to handle the complexity resulting from the subgraph isomorphism testing. The ....

Sobik, F. and Sommerfeld, E. (1982). A graph theoretic approach for representation and classification of structured objects. In Proceedings of the 5.th European Conference on Artificial Intelligence, ECAI.


Relational Learning with Decision Trees - Geibel, Wysotzki (1996)   (4 citations)  (Correct)

....we want to use decision trees for relational structures, the main problem is to construct suitable attributes for graphs or nodes, respectively. Such attributes must capture the elementary attributes of the nodes as well as structural properties of the considered graph. One possibility examined in [9] is the usage of subgraphs as attributes. To determine the value for an subgraph attribute, one has to check, how often the subgraph occurs in the tested graph. The main problem with this approach is to determine the subgraphs to be used as attributes and the complexity resulting from the subgraph ....

F. Sobik and E. Sommerfeld, `A graph theoretic approach for representation and classification of structured objects', in Proceedings of the 5.th European Conference on Artificial Intelligence, ECAI, (1982).

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