Article

Boosting for regression transfer

Authors:

Peter StoneAuthors Info & Claims

ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning

Pages 863 - 870

Published: 21 June 2010 Publication History

Abstract

The goal of transfer learning is to improve the learning of a new target concept given knowledge of related source concept(s). We introduce the first boosting-based algorithms for transfer learning that apply to regression tasks. First, we describe two existing classification transfer algorithms, ExpBoost and TrAdaBoost, and show how they can be modified for regression. We then introduce extensions of these algorithms that improve performance significantly on controlled experiments in a wide range of test domains.

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Cited By

Pan SVermetten DLópez-Ibáñez MBäck TWang HLi XHandl J(2024)Transfer Learning of Surrogate Models via Domain Affine TransformationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654032(385-393)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654032
Li HWang ZZeng NWu PLi Y(2024)Promoting Objective Knowledge Transfer: A Cascaded Fuzzy System for Solving Dynamic Multiobjective Optimization ProblemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.344320732:11(6199-6213)Online publication date: 1-Nov-2024
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Information

Published In

cover image Guide Proceedings

ICML'10: Proceedings of the 27th International Conference on International Conference on Machine Learning

June 2010

1262 pages

ISBN:9781605589077

Sponsors

NSF: National Science Foundation
Xerox
Microsoft Research: Microsoft Research
Yahoo!
IBM: IBM

Publisher

Omnipress

Madison, WI, United States

Publication History

Published: 21 June 2010

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Cited By

Pan SVermetten DLópez-Ibáñez MBäck TWang HLi XHandl J(2024)Transfer Learning of Surrogate Models via Domain Affine TransformationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654032(385-393)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654032
Li HWang ZZeng NWu PLi Y(2024)Promoting Objective Knowledge Transfer: A Cascaded Fuzzy System for Solving Dynamic Multiobjective Optimization ProblemsIEEE Transactions on Fuzzy Systems10.1109/TFUZZ.2024.344320732:11(6199-6213)Online publication date: 1-Nov-2024
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Luo HPaal S(2021)Reducing the effect of sample bias for small data sets with double‐weighted support vector transfer regressionComputer-Aided Civil and Infrastructure Engineering10.1111/mice.1261736:3(248-263)Online publication date: 15-Feb-2021
https://dl.acm.org/doi/10.1111/mice.12617
Al-Helali BChen QXue BZhang MCoello Coello C(2020)Multi-tree genetic programming for feature construction-based domain adaptation in symbolic regression with incomplete dataProceedings of the 2020 Genetic and Evolutionary Computation Conference10.1145/3377930.3390160(913-921)Online publication date: 25-Jun-2020
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Nguyen BXue BAndreae PTakadama K(2018)A particle swarm optimization based feature selection approach to transfer learning in classificationProceedings of the Genetic and Evolutionary Computation Conference10.1145/3205455.3205540(37-44)Online publication date: 2-Jul-2018
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Wei PSagarna RKe YOng YGoh C(2017)Source-target similarity modelings for multi-source transfer Gaussian process regressionProceedings of the 34th International Conference on Machine Learning - Volume 7010.5555/3305890.3306066(3722-3731)Online publication date: 6-Aug-2017
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Segev NHarel MMannor SCrammer KEl-Yaniv R(2017)Learn on Source, Refine on Target: A Model Transfer Learning Framework with Random ForestsIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2016.261811839:9(1811-1824)Online publication date: 1-Sep-2017
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