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dc.contributor.authorFoulds, James Richard
dc.contributor.authorFrank, Eibe
dc.coverage.spatialConference held at Canberra, Australiaen_NZ
dc.identifier.citationFoulds, J.R. & Frank, E. (2010). Speeding up and boosting diverse density learning. In B. Pfahringer, G. Holmes & A. Hoffmann (Eds.), LNAI 6332, Discovery Science, Proceedings of 13th International Conferecne, DS2010, Canberra, Australia, October 6-8 2010 (pp. 102-116). Berlin, Germany: Springer.en_NZ
dc.description.abstractIn multi-instance learning, each example is described by a bag of instances instead of a single feature vector. In this paper, we revisit the idea of performing multi-instance classification based on a point-and-scaling concept by searching for the point in instance space with the highest diverse density. This is a computationally expensive process, and we describe several heuristics designed to improve runtime. Our results show that simple variants of existing algorithms can be used to find diverse density maxima more efficiently. We also show how significant increases in accuracy can be obtained by applying a boosting algorithm with a modified version of the diverse density algorithm as the weak learner.en_NZ
dc.source13th International Conference on Discovery Science (DS)en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectdata miningen_NZ
dc.subjectmulti-instance learningen_NZ
dc.subjectMachine learning
dc.titleSpeeding up and boosting diverse density learningen_NZ
dc.typeConference Contributionen_NZ
dc.relation.isPartOfProceedings of 13th International Conference on Discovery Science (DS 2010)en_NZ
pubs.volumeLNAI 6332, Lecture Notes in Artificial Intelligenceen_NZ

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