Supervised Learning From Embedded Subgraphs
| dc.contributor | Potts, Joseph T. | en_US |
| dc.date.accessioned | 2007-08-23T01:56:25Z | |
| dc.date.accessioned | 2011-08-24T21:40:07Z | |
| dc.date.available | 2007-08-23T01:56:25Z | |
| dc.date.available | 2011-08-24T21:40:07Z | |
| dc.date.issued | 2007-08-23T01:56:25Z | |
| dc.date.submitted | May 2006 | en_US |
| dc.description.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. | en_US |
| dc.identifier.uri | http://hdl.handle.net/10106/283 | |
| dc.language.iso | EN | en_US |
| dc.publisher | Computer Science & Engineering | en_US |
| dc.title | Supervised Learning From Embedded Subgraphs | en_US |
| dc.type | Ph.D. | en_US |