Cited by 591Domain Adaptation and Few-Shot LearningMachine Learning and AlgorithmsMachine Learning and Data Classification

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Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy

Bruzzone, L., Marconcini, M.·2010·IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI 10.1109/tpami.2009.57

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Abstract

This paper addresses pattern classification in the framework of domain adaptation by considering methods that solve problems in which training data are assumed to be available only for a source domain different (even if related) from the target domain of (unlabeled) test data. Two main novel contributions are proposed: 1) a domain adaptation support vector machine (DASVM) technique which extends the formulation of support vector machines (SVMs) to the domain adaptation framework and 2) a circular indirect accuracy assessment strategy for validating the learning of domain adaptation classifiers when no true labels for the target--domain instances are available. Experimental results, obtained on a series of two-dimensional toy problems and on two real data sets related to brain computer interface and remote sensing applications, confirmed the effectiveness and the reliability of both the DASVM technique and the proposed circular validation strategy.

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Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Statistical Learning Theory
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
On Information and Sufficiency
Kullback, S., & Leibler, R. A. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729694 · Cited by 20,193
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,043
Multitask Learning
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Divergence measures based on the Shannon entropy
Lin, J.-H. (1991) · IEEE Transactions on Information Theory · doi:10.1109/18.61115 · Cited by 5,877
Advances in kernel methods: support vector learning
Schölkopf, B., Burges, C. J. C., & Smola, A. J. (1999) · doi:10.5555/299094 · Cited by 5,809
Fast Training of Support Vector Machines Using Sequential Minimal Optimization
Platt, J. C. (1998) · The MIT Press eBooks · doi:10.7551/mitpress/1130.003.0016 · Cited by 5,501
An Introduction to Support Vector Machines
Cristianini, N., & Shawe-Taylor, J. S. (2000) · ePrints Soton (University of Southampton) · Cited by 4,115

Show all 54 references

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Authors: Bruzzone, L., & Marconcini, M.
Affiliations: University of Trento (IT)
ORCID: Bruzzone, L.
Marconcini, M.
Venue: IEEE Transactions on Pattern Analysis and Machine Intelligence
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2010-05
Volume: 32
Issue: 5
Pages: 770-787
ISSN: 0162-8828 (ISSN-L)
DOI: 10.1109/tpami.2009.57
OpenAlex: W2159570078
Cited by: 591
References: 54
Citations by year: 2026: 4 · 2025: 12 · 2024: 21 · 2023: 25 · 2022: 40 · 2021: 42 · 2020: 70 · 2019: 64 · 2018: 64 · 2017: 47 · 2016: 51 · 2015: 54 · 2014: 45 · 2013: 19 · 2012: 21
FWCI: 11.91
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Locations: IEEE Transactions on Pattern Analysis and Machine Intelligence (publishedVersion)
PubMed (publishedVersion)
Institutional Research Information System (Università degli Studi di Trento) (submittedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords: domain adaptation, support vector machine, classifier validation, unlabeled target data, brain-computer interface, remote sensing, cross-domain classification
MeSH terms: Algorithms, Artificial Intelligence, Computer Simulation, Models, Theoretical, Pattern Recognition, Automated

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Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy

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