Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning
aut.embargo | No | en |
aut.thirdpc.contains | No | |
aut.thirdpc.permission | No | |
aut.thirdpc.removed | No | |
dc.contributor.advisor | Shaoning, Pang | |
dc.contributor.advisor | Nikola, Kasabov | |
dc.contributor.author | Fan, Liu | |
dc.date.accessioned | 2011-02-08T00:14:12Z | |
dc.date.available | 2011-02-08T00:14:12Z | |
dc.date.copyright | 2011 | |
dc.date.issued | 2011 | |
dc.date.updated | 2011-02-07T23:37:26Z | |
dc.description.abstract | Multi-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.uri | https://hdl.handle.net/10292/1120 | |
dc.language.iso | en | en_NZ |
dc.publisher | Auckland University of Technology | |
dc.rights.accessrights | OpenAccess | |
dc.subject | Multi-task Learning | |
dc.subject | Knowledge Transfer | |
dc.subject | Correlated multi-task learning | |
dc.subject | Minimum Enclosing Ball | |
dc.subject | Machine Learning | |
dc.subject | Knowledge Sharing | |
dc.subject | Learner Independence | |
dc.title | Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning | |
dc.type | Thesis | |
thesis.degree.grantor | Auckland University of Technology | |
thesis.degree.level | Masters Theses | |
thesis.degree.name | Master of Computer and Information Sciences |