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A New Modeling for Knowledge Transfer in Machine Learning by Fan Liu (Paperback, 2011)

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Product Information

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.

Product Identifiers

Publisher
Lap Lambert Academic Publishing
ISBN-13
9783844397321
eBay Product ID (ePID)
108659297

Product Key Features

Author
Fan Liu
Publication Name
A New Modeling for Knowledge Transfer in Machine Learning
Format
Paperback
Language
English
Subject
Engineering & Technology
Publication Year
2011
Type
Textbook
Number of Pages
88 Pages

Dimensions

Item Height
229mm
Item Width
152mm
Item Weight
141g

Additional Product Features

Title_Author
Fan Liu
Country/Region of Manufacture
Germany

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