Learning from Examples in a Single Graph

All Papers

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.