Minimum Enclosing Ball-Based Learner Independent Knowledge Transfer for Correlated Multi-Task Learning
Date
Authors
Fan, Liu
Supervisor
Shaoning, Pang
Nikola, Kasabov
Item type
Thesis
Degree name
Journal Title
Journal ISSN
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