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dc.contributor.authorFoulds, James Richard
dc.contributor.authorFrank, Eibe
dc.date.accessioned2010-05-11T02:38:17Z
dc.date.available2010-05-11T02:38:17Z
dc.date.issued2010
dc.identifier.citationFoulds, J. & Frank, E. (2010). A review of multi-instance learning assumptions. The Knowledge Engineering Review, 25(1), 1-25.en
dc.identifier.urihttps://hdl.handle.net/10289/3870
dc.description.abstractMulti-instance (MI) learning is a variant of inductive machine learning, where each learning example contains a bag of instances instead of a single feature vector. The term commonly refers to the supervised setting, where each bag is associated with a label. This type of representation is a natural fit for a number of real-world learning scenarios, including drug activity prediction and image classification, hence many MI learning algorithms have been proposed. Any MI learning method must relate instances to bag-level class labels, but many types of relationships between instances and class labels are possible. Although all early work in MI learning assumes a specific MI concept class known to be appropriate for a drug activity prediction domain; this ‘standard MI assumption’ is not guaranteed to hold in other domains. Much of the recent work in MI learning has concentrated on a relaxed view of the MI problem, where the standard MI assumption is dropped, and alternative assumptions are considered instead. However, often it is not clearly stated what particular assumption is used and how it relates to other assumptions that have been proposed. In this paper, we aim to clarify the use of alternative MI assumptions by reviewing the work done in this area.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherCambridge University Pressen_NZ
dc.relation.urihttp://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=7415676en
dc.rightsThis article has been published in the journal: The Knowledge Engineering Review. Copyright © Cambridge University Press 2010.
dc.subjectcomputer scienceen
dc.subjectMachine learningen
dc.subjectmulti-instanceen
dc.titleA review of multi-instance learning assumptionsen
dc.typeJournal Articleen
dc.identifier.doi10.1017/S026988890999035Xen
dc.relation.isPartOfKnowledge Engineering Reviewen_NZ
pubs.begin-page1en_NZ
pubs.elements-id34895
pubs.end-page25en_NZ
pubs.issue1en_NZ
pubs.volume25en_NZ


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