A New Modeling for Knowledge Transfer in Machine Learning | Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning

Fan Liu

ISBN 10: 3844397329 / ISBN 13: 9783844397321

Published by LAP LAMBERT Academic Publishing, 2011

About this Item

A New Modeling for Knowledge Transfer in Machine Learning | Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning | Fan Liu | Taschenbuch | 88 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783844397321 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Bibliographic Details

• Title: A New Modeling for Knowledge Transfer in Machine Learning | Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning
• Publisher: LAP LAMBERT Academic Publishing
• Publication Date: 2011
• Language: English
• ISBN 10: 3844397329
• ISBN 13: 9783844397321
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 220W x 150H x 6D millimeters 220 millimeters width by 150 millimeters height by 6 millimeters depth
• Weight: 149 grams
• Seller catalogs:
  • Bücher

About this title

Synopsis

Multi-Task Learning (MTL), as opposed to Single Task Learning (STL), has become a hot topic in machine learning research. MTL has shown significant advantage to STL because of its ability to facilitate knowledge sharing between tasks. This thesis presents my 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 correlated MTL adapts learner independence, thus empowering any ordinary classifier for MTL. The proposed MEB-based KT is on the basis that in the feature space, the two correlated tasks share some common input data that lie on the overlapping regions of the feature spaces in-between the two correlated tasks. The main idea is to find the correlating knowledge – overlapping regions of the two tasks – and transfer the related data regardless of the learner employed. KT is done by building a correlation space via MEBs and transferring the enclosed instances from the primary task to the secondary task. The extent of KT depends on the amount of overlapping instances between two tasks. This book is required reading for post-graduates and researchers in MTL.

About the Author

Fan Liu has received a MCIS at AUT, New Zealand, in 2011. The MCIS research focuses on Co-Learning Multi-Task Pattern Recognition using Minimum Enclosing Balls. Fan has been awarded a scholarship in connection with the NICT Project that KEDRI is involved with. Currently, Fan is a Doctoral Candidate and interested in Swarm Intelligence for Robotics.