Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning

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aut.embargoNoen
aut.thirdpc.containsNo
aut.thirdpc.permissionNo
aut.thirdpc.removedNo
dc.contributor.advisorShaoning, Pang
dc.contributor.advisorNikola, Kasabov
dc.contributor.authorFan, Liu
dc.date.accessioned2011-02-08T00:14:12Z
dc.date.available2011-02-08T00:14:12Z
dc.date.copyright2011
dc.date.issued2011
dc.date.updated2011-02-07T23:37:26Z
dc.description.abstractMulti-Task Learning (MTL), as opposed to Single Task Learning (STL), has become a hot topic in machine learning research. For many real world problems in application areas such as medical decision making, pattern recognition, and finance forecasting – MTL has shown significant advantage to STL because of its ability to facilitate knowledge sharing between tasks. This thesis presents our recent studies on Knowledge Transfer (KT) – the process of transferring knowledge from one task to another, which is at the core of MTL. The novelly proposed KT algorithm for correlation multi-task machine learning adapts learner independence into MTL, thus empowering any ordinary classifier for MTL.
dc.identifier.urihttps://hdl.handle.net/10292/1120
dc.language.isoenen_NZ
dc.publisherAuckland University of Technology
dc.rights.accessrightsOpenAccess
dc.subjectMulti-task Learning
dc.subjectKnowledge Transfer
dc.subjectCorrelated multi-task learning
dc.subjectMinimum Enclosing Ball
dc.subjectMachine Learning
dc.subjectKnowledge Sharing
dc.subjectLearner Independence
dc.titleMinimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning
dc.typeThesis
thesis.degree.grantorAuckland University of Technology
thesis.degree.levelMasters Theses
thesis.degree.nameMaster of Computer and Information Sciences
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