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dc.contributor.authorDong, Linen_NZ
dc.date.accessioned2008-10-23T11:55:44Z
dc.date.available2008-11-21T12:39:17Z
dc.date.issued2006en_NZ
dc.identifier.citationDong, L. (2006). A Comparison of Multi-instance Learning Algorithms (Thesis, Master of Science (MSc)). The University of Waikato, Hamilton, New Zealand. Retrieved from https://hdl.handle.net/10289/2453en
dc.identifier.urihttps://hdl.handle.net/10289/2453
dc.description.abstractMotivated by various challenging real-world applications, such as drug activity prediction and image retrieval, multi-instance (MI) learning has attracted considerable interest in recent years. Compared with standard supervised learning, the MI learning task is more difficult as the label information of each training example is incomplete. Many MI algorithms have been proposed. Some of them are specifically designed for MI problems whereas others have been upgraded or adapted from standard single-instance learning algorithms. Most algorithms have been evaluated on only one or two benchmark datasets, and there is a lack of systematic comparisons of MI learning algorithms. This thesis presents a comprehensive study of MI learning algorithms that aims to compare their performance and find a suitable way to properly address different MI problems. First, it briefly reviews the history of research on MI learning. Then it discusses five general classes of MI approaches that cover a total of 16 MI algorithms. After that, it presents empirical results for these algorithms that were obtained from 15 datasets which involve five different real-world application domains. Finally, some conclusions are drawn from these results: (1) applying suitable standard single-instance learners to MI problems can often generate the best result on the datasets that were tested, (2) algorithms exploiting the standard asymmetric MI assumption do not show significant advantages over approaches using the so-called collective assumption, and (3) different MI approaches are suitable for different application domains, and no MI algorithm works best on all MI problems.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.publisherThe University of Waikatoen_NZ
dc.rightsAll items in Research Commons are provided for private study and research purposes and are protected by copyright with all rights reserved unless otherwise indicated.
dc.subjectmachine learningen_NZ
dc.subjectmulti-instance learningen_NZ
dc.titleA Comparison of Multi-instance Learning Algorithmsen_NZ
dc.typeThesisen_NZ
thesis.degree.disciplineSchool of Computing and Mathematical Sciencesen_NZ
thesis.degree.grantorUniversity of Waikatoen_NZ
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (MSc)en_NZ
uow.date.accession2008-10-23T11:55:44Zen_NZ
uow.date.available2008-11-21T12:39:17Zen_NZ
uow.identifier.adthttp://adt.waikato.ac.nz/public/adt-uow20081023.115544en_NZ
uow.date.migrated2009-06-09T23:32:08Zen_NZ
pubs.place-of-publicationHamilton, New Zealanden_NZ


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