A New Modeling for Knowledge Transfer in Machine Learning: Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning - Softcover
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.
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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.
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A New Modeling for Knowledge Transfer in Machine Learning
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LAP LAMBERT Academic Publishing Mai 2011, 2011
ISBN 10: 3844397329
ISBN 13: 9783844397321
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 88 pp. Englisch. Seller Inventory # 9783844397321
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A New Modeling for Knowledge Transfer in Machine Learning
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A New Modeling for Knowledge Transfer in Machine Learning
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A New Modeling for Knowledge Transfer in Machine Learning
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Liu FanFan 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 Pr. Seller Inventory # 5477200
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A New Modeling for Knowledge Transfer in Machine Learning
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VDM Verlag Dr. Mueller Aktiengesellschaft & Co. KG, 2011
ISBN 10: 3844397329
ISBN 13: 9783844397321
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A New Modeling for Knowledge Transfer in Machine Learning
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ISBN 13: 9783844397321
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -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.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 88 pp. Englisch. Seller Inventory # 9783844397321
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A New Modeling for Knowledge Transfer in Machine Learning : Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning
Published by
LAP LAMBERT Academic Publishing, 2011
ISBN 10: 3844397329
ISBN 13: 9783844397321
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Taschenbuch
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Seller Inventory # 9783844397321
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A New Modeling for Knowledge Transfer in Machine Learning | Minimum Enclosing Ball-based Learner Independent Knowledge Transfer for Correlated Multi-task Learning
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Taschenbuch. Condition: Neu. 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. Seller Inventory # 107007243