A New Modeling for Knowledge Transfer in Machine Learning

Language: English

Published by LAP LAMBERT Academic Publishing Mai 2011, 2011

3844397329 / 9783844397321

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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

Title
A New Modeling for Knowledge Transfer in Machine Learning
Author
Fan Liu
Publisher
LAP LAMBERT Academic Publishing Mai 2011
Publication year
2011
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 10
3844397329
ISBN 13
9783844397321
Item weight
149 grams
Dimensions
220x150x6 mm

buchversandmimpf2000

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