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- W2956227 abstract "We develop a machine learning algorithm which learns rules for classification from training examples in a graph representation. However, unlike most other such algorithms which use one graph for each example, ours allows all of the training examples to be in a single, connected graph. We employ the Minimum Description Length principle to produce a novel performance metric for judging the value of a learned classification. We implement the algorithm by extending the Subdue graph-based learning system. Finally, we demonstrate the use of the new system in two different domains, earth science and homeland security." @default.
- W2956227 created "2016-06-24" @default.
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- W2956227 date "2006-01-01" @default.
- W2956227 modified "2023-09-23" @default.
- W2956227 title "Supervised learning from embedded subgraphs" @default.
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