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Minimum Enclosing Ball-Based Learner Independent Knowledge Transfer for Correlated Multi-Task Learning

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Whole thesis(668.66 KB)

Date

2011

Authors

Supervisor

Shaoning, Pang
Nikola, Kasabov

Item type

Thesis

Degree name

Master of Computer and Information Sciences

Journal Title

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Volume Title

Publisher

Auckland University of Technology

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.

Description

Keywords

Multi-task Learning, Knowledge Transfer, Correlated multi-task learning, Minimum Enclosing Ball, Machine Learning, Knowledge Sharing, Learner Independence

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Permanent link

https://hdl.handle.net/10292/1120

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Masters Theses