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dc.contributor.authorSeeland, Madeleine
dc.contributor.authorBuchwald, Fabian
dc.contributor.authorKramer, Stefan
dc.contributor.authorPfahringer, Bernhard
dc.date.accessioned2012-09-17T02:56:38Z
dc.date.available2012-09-17T02:56:38Z
dc.date.copyright2012
dc.date.issued2012
dc.identifier.citationSeeland, M., Buchwald, F., Kramer, S. & Pfahringer, B. (2012). Maximum Common Subgraph based locally weighted regression. In Proceedings of the 27th Annual ACM Symposium on Applied Computing (SAC '12). ACM, New York, 165-172.en_NZ
dc.identifier.urihttp://hdl.handle.net/10289/6631
dc.description.abstractThis paper investigates a simple, yet effective method for regression on graphs, in particular for applications in chem-informatics and for quantitative structure-activity relationships (QSARs). The method combines Locally Weighted Learning (LWL) with Maximum Common Subgraph (MCS) based graph distances. More specifically, we investigate a variant of locally weighted regression on graphs (structures) that uses the maximum common subgraph for determining and weighting the neighborhood of a graph and feature vectors for the actual regression model. We show that this combination, LWL-MCS, outperforms other methods that use the local neighborhood of graphs for regression. The performance of this method on graphs suggests it might be useful for other types of structured data as well.en_NZ
dc.language.isoen
dc.publisherACMen_NZ
dc.relation.ispartofProceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12
dc.titleMaximum Common Subgraph based locally weighted regressionen_NZ
dc.typeConference Contributionen_NZ
dc.identifier.doi10.1145/2245276.2245309en_NZ
pubs.elements-id22282


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