Preference inference based on maximising margin

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dc.contributor.advisor Wilson, Nic en
dc.contributor.advisor O'Sullivan, Barry en
dc.contributor.author Montazery Hedeshi, Mojtaba
dc.date.accessioned 2018-08-03T09:17:28Z
dc.date.available 2018-08-03T09:17:28Z
dc.date.issued 2018
dc.date.submitted 2018
dc.identifier.citation Montazery Hedeshi, M. 2018. Preference inference based on maximising margin. PhD Thesis, University College Cork. en
dc.identifier.endpage 175 en
dc.identifier.uri http://hdl.handle.net/10468/6572
dc.description.abstract In a decision-making problem, where we need to choose a particular decision from a set of possible choices, the user often has some preferences which determine if one decision is preferred over another. When the number of choices is large, an intelligent system can help the user by attempting to learn user preferences. One way of learning user preferences is based on the maximum margin approach, where maximising the margin can be seen as satisfying each existing preference input to the greatest degree. In this thesis, we first apply this method to a real-world application, ride-sharing, and examine its potential effectiveness. Nevertheless, we show that the maximum margin preference learning approach is sensitive to the way that preferences inputs and features are scaled. We explain why it is naturally expected that a preference relation is scaling invariant, and go on to construct and characterise some preference relations that are invariant to the scaling of (i) preferences inputs, (ii) features, and (iii) both preferences inputs and features simultaneously. We compare these relations and propose two algorithms to find the optimal elements according to each relation. In the last main chapter, we argue that the rescaling of features is also an issue in the standard SVM classification and propose a new form of more conservative classification that is invariant to the rescaling of features. We argue that this cautious way of classification could be helpful in some critical decision-making applications. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher University College Cork en
dc.rights © 2018, Mojtaba Montazery Hedeshi. en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/ en
dc.subject Preference learning en
dc.subject Preference reasoning en
dc.subject Artificial intelligence en
dc.title Preference inference based on maximising margin en
dc.type Doctoral thesis en
dc.type.qualificationlevel Doctoral en
dc.type.qualificationname PhD en
dc.internal.availability Full text available en
dc.check.info Not applicable en
dc.description.version Accepted Version
dc.contributor.funder Science Foundation Ireland en
dc.description.status Not peer reviewed en
dc.internal.school Computer Science and Information Technology en
dc.check.type No Embargo Required
dc.check.reason Not applicable en
dc.check.opt-out Not applicable en
dc.thesis.opt-out false
dc.check.embargoformat Embargo not applicable (If you have not submitted an e-thesis or do not want to request an embargo) en
ucc.workflow.supervisor n.wilson@ucc.ie
dc.internal.conferring Autumn 2018 en
dc.internal.ricu Insight - Centre for Data Analytics 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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© 2018, Mojtaba Montazery Hedeshi. Except where otherwise noted, this item's license is described as © 2018, Mojtaba Montazery Hedeshi.
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