NDLI: Set-Based Boosting for Instance-Level Transfer

Content Provider	IEEE Xplore Digital Library
Author	Eaton, E. desJardins, M.
Copyright Year	2009
Abstract	The success of transfer to improve learning on a target task is highly dependent on the selected source data. Instance-based transfer methods reuse data from the source tasks to augment the training data for the target task. If poorly chosen, this source data may inhibit learning, resulting in negative transfer. The current best performing algorithm for instance-based transfer, TrAdaBoost, performs poorly when given irrelevant source data. We present a novel set-based boosting technique for instance-based transfer. The proposed algorithm, TransferBoost, boosts both individual instances and collective sets of instances from each source task. In effect, TransferBoost boosts each source task, assigning higher weight to those source tasks which show positive transferability to the target task, and then adjusts the weights of the instances within each source task via AdaBoost. The results demonstrate that TransferBoost significantly improves transfer performance over existing instance-based algorithms when given a mix of relevant and irrelevant source data.
Starting Page	422
Ending Page	428
File Size	322405
Page Count	7
File Format	PDF
ISBN	9781424453849
DOI	10.1109/ICDMW.2009.97
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-12-06
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Machine learning algorithms Costs USA Councils Investments Laboratories Training data Boosting Iterative algorithms Data mining Knowledge transfer
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Improving Learner Performance with Data Sampling and Boosting

Bandit-Based Algorithms for Budgeted Learning

A diagnostic tool for tree based supervised classification learning algorithms

Investigation of Web-based teaching and learning by boosting algorithms

Multiple Instance Transfer Learning

Resampling or Reweighting: A Comparison of Boosting Implementations

2009 ieee international conference on data mining workshops set-based boosting for instance-level transfer.

Reduced error specialization based on the information content of rule set

A Density-Based Approach for Instance Selection

Set-Based Boosting for Instance-Level Transfer

Similar Documents

Improving Learner Performance with Data Sampling and Boosting

Bandit-Based Algorithms for Budgeted Learning

A diagnostic tool for tree based supervised classification learning algorithms

Investigation of Web-based teaching and learning by boosting algorithms

Multiple Instance Transfer Learning

Resampling or Reweighting: A Comparison of Boosting Implementations

2009 ieee international conference on data mining workshops set-based boosting for instance-level transfer.

Reduced error specialization based on the information content of rule set

A Density-Based Approach for Instance Selection

Set-Based Boosting for Instance-Level Transfer