The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
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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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
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
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
Research works printed but not linked: 1
- Dai, Transferring Naïve Bayes Classifier for Text Classification
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Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016) · Journal of Big Data · doi:10.1186/s40537-016-0043-6 · Cited by 6,260
Deep visual domain adaptation: A survey
Wang, M., & Deng, W. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.05.083 · Cited by 2,209
Geodesic flow kernel for unsupervised domain adaptation
Gong, B., Shi, Y., Sha, F., & Grauman, K. L. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6247911 · Cited by 2,278
Deep Hashing Network for Unsupervised Domain Adaptation
Venkateswara, H., Eusebio, J., Chakraborty, S., & Panchanathan, S. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.572 · Cited by 2,171
Transfer Feature Learning with Joint Distribution Adaptation
Long, M., Wang, J., Ding, G., Sun, J., & Yu, P. S. (2013) · 2013 IEEE International Conference on Computer Vision · doi:10.1109/iccv.2013.274 · Cited by 2,071
Metadata
- Authors
- Bruzzone, L., & Marconcini, M.
- Affiliations
- University of Trento (IT)
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2010-05
- Volume
- 32
- Issue
- 5
- Pages
- 770-787
- ISSN
- 0162-8828 (ISSN-L)
- 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
- 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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