Dominance and optimisation based on scale-invariant maximum margin preference learning

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dc.contributor.author Montazery, Mojtaba
dc.contributor.author Wilson, Nic
dc.date.accessioned 2020-12-01T16:25:51Z
dc.date.available 2020-12-01T16:25:51Z
dc.date.issued 2017-08
dc.identifier.citation Montazery, M. and Wilson, N. (2017) 'Dominance and Optimisation Based on Scale-Invariant Maximum Margin Preference Learning', IJCAI'17: Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia 19-25 August, pp. 1209-1215. doi: 10.24963/ijcai.2017/168 en
dc.identifier.startpage 1209 en
dc.identifier.endpage 1215 en
dc.identifier.isbn 978-0-9992411-0-3
dc.identifier.uri http://hdl.handle.net/10468/10799
dc.identifier.doi 10.24963/ijcai.2017/168 en
dc.description.abstract In the task of preference learning, there can be natural invariance properties that one might often expect a method to satisfy. These include (i) invariance to scaling of a pair of alternatives, e.g., replacing a pair ( a,b ) by (2 a ,2 b ); and (ii) invariance to rescaling of features across all alternatives. Maximum margin learning approaches satisfy such invariance properties for pairs of test vectors, but not for the preference input pairs, i.e., scaling the inputs in a different way could result in a different preference relation. In this paper we define and analyse more cautious preference relations that are invariant to the scaling of features, or inputs, or both simultaneously; this leads to computational methods for testing dominance with respect to the induced relations, and for generating optimal solutions among a set of alternatives. In our experiments, we compare the relations and their associated optimality sets based on their decisiveness, computation time and cardinality of the optimal set. We also discuss connections with imprecise probability. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher International Joint Conferences on Artificial Intelligence en
dc.relation.uri https://www.ijcai.org/Proceedings/2017
dc.rights © 2017 International Joint Conferences on Artificial Intelligence en
dc.subject Artificial intelligence (AI) en
dc.subject AI technology en
dc.subject Preference learning en
dc.subject Knowledge representation, reasoning, and logic: Preferences en
dc.subject Machine Learning: learning preferences or rankings en
dc.subject Machine learning en
dc.subject Uncertainty in AI en
dc.subject Knowledge representation en
dc.title Dominance and optimisation based on scale-invariant maximum margin preference learning en
dc.type Conference item en
dc.internal.authorcontactother Nic Wilson, Computer Science, University College Cork, Cork, Ireland. +353-21-490-3000 Email: n.wilson@ucc.ie en
dc.internal.availability Full text available en
dc.date.updated 2020-11-04T13:14:01Z
dc.description.version Accepted Version en
dc.internal.rssid 542655617
dc.contributor.funder Science Foundation Ireland en
dc.description.status Peer reviewed en
dc.internal.copyrightchecked No
dc.internal.licenseacceptance Yes en
dc.internal.conferencelocation Melbourne, Australia en
dc.internal.IRISemailaddress n.wilson@ucc.ie en
dc.relation.project info:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/ en


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