Learning from Examples in a Single Graph

Joseph T. Potts, Diane J. Cook, and Lawrence B. Holder, The University of Texas at Arlington

Of all of the existing learning systems, few are capable of accepting graphs as input. Yet graphs are a powerful data representation capable of efficiently conveying relationships in the data to those who use them, both machine and human. But even among the systems capable of reading graph-based data, most require the examples for each class to be in disjoint graphs. We introduce a learner that can use a single, connected graph with the training examples embedded therein. We propose a new metric to determine the value of a classification. Finally we present the results of a learning experiment on sea surface temperature data.

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