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Maximum Common Subgraph based locally weighted regression

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dc.contributor.author Seeland, Madeleine
dc.contributor.author Buchwald, Fabian
dc.contributor.author Kramer, Stefan
dc.contributor.author Pfahringer, Bernhard
dc.date.accessioned 2012-09-17T02:56:38Z
dc.date.available 2012-09-17T02:56:38Z
dc.date.copyright 2012
dc.date.issued 2012
dc.identifier.citation Seeland, 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.uri http://hdl.handle.net/10289/6631
dc.description.abstract This 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.iso en
dc.publisher ACM en_NZ
dc.relation.ispartof Proceedings of the 27th Annual ACM Symposium on Applied Computing - SAC '12
dc.title Maximum Common Subgraph based locally weighted regression en_NZ
dc.type Conference Contribution en_NZ
dc.identifier.doi 10.1145/2245276.2245309 en_NZ


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