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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
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Research works printed but not linked: 1
- Dai, Transferring Naïve Bayes Classifier for Text Classification
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Low-Rank Constrained Latent Domain Adaptation Co-Regression for Robust Depression Recognition
Tao, J., Xu, H., & Fu, J. (2019) · IEEE Access · doi:10.1109/access.2019.2944211 · Cited by 5
Transresnet: Transferable Resnet For Domain Adaptation
Zheng, J., Wu, W., Zhao, Y., & Fu, H. (2021) · Proceedings - International Conference on Image Processing · doi:10.1109/icip42928.2021.9506562 · Cited by 5
Sparse Domain Adaptation in Projection Spaces Based on Good Similarity Functions
Morvant, E., Habrard, A., & Ayache, S. (2011) · Proceedings · doi:10.1109/icdm.2011.136 · Cited by 5
Label Space Embedding of Manifold Alignment for Domain Adaption
Wang, J., Li, X., & Du, J. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9822-8 · Cited by 5
An Extended Framework for Marginalized Domain AdaptationPreprint
Csurka, G., Chidlovskii, B., Clinchant, S., & Michel, S. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1702.05993 · Cited by 5
A Deeper Look at Dataset BiasPreprint
Tommasi, T., Patricia, N., Barbara Caputo, B., & Tuytelaars, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1505.01257 · Cited by 14
Domain adaptive deep possibilistic clustering for EEG-based emotion recognition
Dan, Y., Li, Q., Wang, X., & Zhou, D. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1592070 · Cited by 4
Rahman, M. G., & Islam, M. Z. (2022) · IEEE Transactions on Services Computing · doi:10.1109/tsc.2022.3213238 · Cited by 4
Idrissi, M. I., Bousquet, N., Gamboa, F., Iooss, B., & Loubes, J. (2024) · Electronic Journal of Statistics · doi:10.1214/24-ejs2268 · Cited by 8
Distribution matching and structure preservation for domain adaptation
Li, P., Ni, Z., Zhu, X., & Song, J. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-022-00887-3 · Cited by 4
Partial label learning with heterogeneous domain adaptation
Zhao, L., Xiao, Y., & Liu, B. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127822 · Cited by 4
Domain adaptation with Hidden Markov Random Fields
Jacobs, J.-P., Thoonen, G., Tuia, D., Camps‐Valls, G., Haest, B., & Scheunders, P. (2013) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2013.6723485 · Cited by 4
Keybook: Unbias object recognition using keywords
Hoo, W. L., Lim, C. H., & Chan, C. S. (2015) · Expert Systems with Applications · doi:10.1016/j.eswa.2015.01.019 · Cited by 4
Extreme Learning Machine-Based Traffic Incidents Detection with Domain Adaptation Transfer Learning
Hatri, C. E., Tahifa, M., & Boumhidi, J. (2016) · Journal of Intelligent Systems · doi:10.1515/jisys-2016-0028 · Cited by 4
Nonlinear Embedding Transform for Unsupervised Domain Adaptation
Venkateswara, H., Chakraborty, S., & Panchanathan, S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-49409-8_36 · Cited by 4
Two-Sided Wasserstein Procrustes Analysis
Jin, K., Liu, C., & Xia, C. H. (2021) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2021/484 · Cited by 4
Dan, Y., Tao, J., & Zhou, D. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.855421 · Cited by 4
Joint subspace and discriminative learning for self-paced domain adaptation
Liu, C., Wu, S., Cao, W., Shen, W., Jiang, D., Yu, Z., & Wong, H. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.106285 · Cited by 4
Robust multi-source co-adaptation with adaptive loss minimization
Tao, J., Dan, Y., & Zhou, D. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116455 · Cited by 4
Domain Invariant and Class Discriminative Heterogeneous Domain Adaptation
Wang, Y., Huang, J., Shang, J., Niu, C., & Zhou, Z. (2018) · International Conference on Communication and Information Systems (ICCIS) · doi:10.1109/icomis.2018.8644888 · Cited by 4
Domain Adaptation with Randomized Expectation MaximizationPreprint
van Laarhoven, T., & Marchiori, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.07634 · Cited by 4
Open Set Domain Adaptation for Image and Action RecognitionPreprint
Busto, P. P., Iqbal, A., & Gall, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.12865 · Cited by 6
Chen, Q., Fang, Z., Li, Z., Du, Q., Deng, S., Jia, T., & Chen, D. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3576362 · Cited by 3
Dantas, C. F., Gaetano, R., Paris, C., & Ienco, D. (2024) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2024.3503756 · Cited by 3
Weakly supervised alignment of image manifolds with semantic ties
Tuia, D. (2014) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2014.6947248 · Cited by 3
Kernel Extreme Learning Machine Based Domain Adaptation
Yang, Y. Y., Xu, C., Yang, R. Q., & Meng, C. (2018) · IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS) · doi:10.1109/ccis.2018.8691325 · Cited by 3
Li, P., Ni, Z., Zhu, X., & Song, J. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-189136 · Cited by 3
Transfer Learning in Reinforcement Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.010 · Cited by 3
Cross-Domain Traffic Scene Understanding by Integrating Deep Learning and Topic Model
Yang, Y., Dong, H., Liu, G., Zhang, L., & Li, L. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/8884669 · Cited by 3
RKHS subspace domain adaption via minimum distribution gap
Qiu, Y., Zhang, C., Xiong, C., Ma, Z., & Liao, S. (2023) · Pattern Analysis and Applications · doi:10.1007/s10044-023-01170-y · Cited by 3
Oh, D., Kang, K., Seo, S., Xiao, J., Jang, K., Kim, K., Park, H., & Won, J.-H. (2023) · Remote Sensing · doi:10.3390/rs15102584 · Cited by 3
Online Domain Adaptive Classification for Mobile-to-Edge Computing
Abkenar, F. S., Badia, L., & Levorato, M. (2023) · IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM) · doi:10.1109/wowmom57956.2023.00016 · Cited by 3
Detection of specific changes in image time series by an adaptive change vector analysis
Zanotta, D. C., Bruzzone, L., & Bovolo, F. (2014) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2014.6946668 · Cited by 3
Self-citationUtilizing Information from Task-Independent Aspects via GAN-Assisted Knowledge Transfer
Fu, L., Li, J., Zhou, L., Ma, Z., Liu, S., Lin, Z., & Prasad, M. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489047 · Cited by 3
Introduce More Characteristics of Samples into Cross-domain Sentiment Classification
Liu, W., & Fu, X. (2018) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2018.8545331 · Cited by 3
Joint Robust Transfer Metric and Adaptive Transfer Function Learning
Azarbarzin, S., & Afsari, F. (2019) · Neural Processing Letters · doi:10.1007/s11063-019-10152-3 · Cited by 3
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.015 · Cited by 3
Video Annotation by Incremental Learning from Grouped Heterogeneous Sources
Wang, H., Song, H., Wu, X., & Jia, Y. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-16814-2_32 · Cited by 3
Distribution Regularized Nonnegative Matrix Factorization for Transfer Visual Feature Learning
Guo, Y., Ding, G., & Liu, Q. (2015) · International Conference on Multimedia Retrieval · doi:10.1145/2671188.2749316 · Cited by 3
Zhang, X., & Wang, S. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2244675 · Cited by 3
Cao, Z., Ge, Y., & Feng, J. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2017.07.032 · Cited by 3
Tao, J., & Xu, H. (2019) · IEEE Access · doi:10.1109/access.2019.2961741 · Cited by 3
Visual Domain Adaptation in the Deep Learning Era
Csurka, G., Hospedales, T. M., Salzmann, M., & Tommasi, T. (2022) · Synthesis lectures on computer vision · doi:10.2200/s01169ed1v01y202202cov020 · Cited by 13
A Domain Adaptation SVM and a Circular Validation Strategy for Land‐Cover Maps Updating
Marconcini, M., & Bruzzone, L. (2009) · doi:10.1002/9780470748992.ch9 · Cited by 3
Self-citationLocality Preserving Joint Transfer for Domain AdaptationPreprint
Li, J., Jing, M., Lü, K., Zhu, L., & Shen, H. T. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1906.07441 · Cited by 3
Chen, Z.-Y., & Sun, M. (2024) · INFORMS journal on computing · doi:10.1287/ijoc.2022.0365 · Cited by 2
A novel graph matching based approach for land-cover classification of multi-temporal images
Banerjee, B., Mohan, B. K., Chaudhuri, S., Bhattacharya, A., & Mohanty, J. (2014) · Indian Conference on Computer Vision, Graphics and Image Processing · doi:10.1145/2683483.2683525 · Cited by 2
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.006 · Cited by 2
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.012 · Cited by 2
Kalita, I., & Roy, M. (2022) · IEEE Region 10 Symposium (TENSYMP) · doi:10.1109/tensymp54529.2022.9864468 · Cited by 2
A biologically inspired architecture with switching units can learn to generalize across backgrounds
Voina, D., Shea-Brown, E., & Mihalas, S. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.014 · Cited by 2
Unsupervised domain adaptation via feature transfer learning based on elastic embedding
Yang, L., Lu, B., Zhou, Q., & Pan, S. (2024) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-023-02082-3 · Cited by 2
Discriminative possibilistic clustering promoting cross-domain emotion recognition
Dan, Y., Zhou, D., & Wang, Z. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1458815 · Cited by 2
Transfer Learning in Bioinformatics
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.023 · Cited by 2
Domain adaptation to automatic classification of neonatal amplitude-integrated EEG
Liu, Y., Chen, W., Yang, S., & Huang, K. (2012) · International Conference on Intelligent Systems Design and Applications (ISDA) · doi:10.1109/isda.2012.6416525 · Cited by 2
Semisupervised domain adaptation for mixture model based classifiers
Raghuram, J., Miller, D. J., & Kesidis, G. (2012) · Conference on Information Sciences and Systems · doi:10.1109/ciss.2012.6310708 · Cited by 2
Samanta, S., & Das, S. (2016) · IET Computer Vision · doi:10.1049/iet-cvi.2015.0322 · Cited by 2
Chakraborty, S., & Roy, M. (2016) · International Conference on Recent Advances in Information Technology · doi:10.1109/rait.2016.7507955 · Cited by 2
Heterogeneous Feature Space Based Task Selection Machine for Unsupervised Transfer Learning
Xue, S., Jie Lu, B., Zhang, G., & Xiong, L. (2015) · International Conference on Intelligent Systems and Knowledge Engineering (ISKE) · doi:10.1109/iske.2015.29 · Cited by 2
Multi-part Representation Learning For Cross-domain Web Content Classification Using Neural Networks
Ganesh, J., Bhatt, H. S., Sinha, M., & Roy, S. (2017) · ACM Conference on Hypertext and Social Media (HT) · doi:10.1145/3078714.3078745 · Cited by 2
Zhou, X. H., & Prasad, S. (2017) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2017.8127787 · Cited by 2
Image Translation by Domain-Adversarial Training
Li, Z., Wang, W., & Zhao, Y. (2018) · Computational Intelligence and Neuroscience · doi:10.1155/2018/8974638 · Cited by 2
Paris, C., Bruzzone, L., & Fernández‐Prieto, D. (2018) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2018.8517458 · Cited by 2
Self-citationTransfer Learning in Activity Recognition
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.024 · Cited by 2
Mardani, M., & Tahmoresnezhad, J. (2021) · Knowledge and Information Systems · doi:10.1007/s10115-021-01586-0 · Cited by 2
Hu, W., Kong, X., Xie, L., Yan, H., Qin, W., Meng, X., Yan, Y., & Yin, E. (2021) · Sensors · doi:10.3390/s21227568 · Cited by 2
Guo, Y., Song, J., Li, M., & Yang, M. (2021) · Earth Science-Journal of China University of Geosciences · doi:10.3799/dqkx.2020.347 · Cited by 2
Transfer Learning in Computer Vision
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.019 · Cited by 2
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.013 · Cited by 2
Zang, S., Li, D., Ma, C., & Ma, J. (2023) · Complexity · doi:10.1155/2023/5072247 · Cited by 2
Li, X., Li, P., Zhang, R., Wang, C., Wei, X., & Zhao, M. (2023) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn54540.2023.10191059 · Cited by 2
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Tao, J., & Wang, S. (2012) · Acta Automatica Sinica · doi:10.3724/sp.j.1004.2012.00797 · Cited by 2
Analyzing Bayesian Crosslingual Transfer in Topic Models
Hao, S., & Paul, M. J. (2019) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.18653/v1/n19-1158 · Cited by 2
Asymmetric alignment joint consistent regularization for multi-source domain adaptation
Shang, J., Niu, C., Zhou, Z., Huang, J., Yang, Z., & Li, X. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09883-6 · Cited by 2
Barbe, A., Gonçalvès, P., Sebban, M., Borgnat, P., Gribonval, R., & Vayer, T. (2021) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai52525.2021.00125 · Cited by 2
Podsiadlo, I., Paris, C., & Bruzzone, L. (2021) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss47720.2021.9553498 · Cited by 2
Self-citationLost in the Past: Recognizing Locations Over Large Time Lags.Preprint
Fernando, B., Tommasi, T., & Tuytelaars, T. (2014) · arXiv (Cornell University) · Cited by 2
Domain Adaptation and Domain Generalization with Representation Learning
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Differentially Private Transfer Learning with Conditionally Deep Autoencoders.Preprint
Kumar, M. (2021) · arXiv (Cornell University) · Cited by 2
Foundations on transfer learning in machine fault diagnosis and prognosis
Yan, R., & Shen, F. (2024) · Elsevier eBooks · doi:10.1016/b978-0-323-99989-2.00011-6 · Cited by 1
Chen, H., Wang, D., Xu, M., Chen, J., Zhang, Y., & Chen, Y. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.129239 · Cited by 1
Domain Adaptation via Learning Using Statistical Invariant
Li, C., Song, Y., & Shao, Y. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3565780 · Cited by 3
Corso, J. D., & Bruzzone, L. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3578766 · Cited by 2
Self-citationLCDAN: Label Confusion Domain Adversarial Network for Information Detection in Public Health Events
Ye, Q., Sun, G., Chen, Y., & Xu, X. (2025) · Electronics · doi:10.3390/electronics14153102 · Cited by 1
Wang, Z., Zhao, Y., Gong, M., Li, H., Liu, G., & Jian-long, T. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111870 · Cited by 1
Geo-location driven image tagging via cross-domain learning
Nie, W., Liu, A.-A., Wang, Z., & Su, Y. (2014) · Multimedia Systems · doi:10.1007/s00530-014-0396-7 · Cited by 1
Habrard, A., Peyrache, J.-P., & Sebban, M. (2011) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2011.35 · Cited by 1
Bridging heterogeneous domains with parallel transport for vision and Multimedia applications
Gopalan, R. (2016) · Uncertainty in Artificial Intelligence · Cited by 1
A New PAC-Bayesian View of Domain Adaptation
Germain, P., Laviolette, F., Habrard, A., & Morvant, E. (2015) · HAL · Cited by 1
Image Clustering under Domain Shift
Gopalan, R. (2017) · IEEE International Conference on Multimedia Big Data · doi:10.1109/bigmm.2017.16 · Cited by 1
Deep Sparse Informative Transfer SoftMax for Cross-Domain Image Classification
Yang, H., Zhou, X., Lin, L., Yao, B., Tan, Z., Tang, H., & Tian, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91458-9_38 · Cited by 1
Localising In Complex Scenes Using Balanced Adversarial Adaptation
Avraham, G., Zuo, Y., & Drummond, T. W. (2020) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv50981.2020.00116 · Cited by 1
Murphy, C. P., & Kerekes, J. P. (2021) · Journal of Spectral Imaging · doi:10.1255/jsi.2021.a2 · Cited by 1
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.011 · Cited by 1
Relation-Based Transfer Learning
Yang, Q., Zhang, Y., Dai, W., & Pan, S. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781139061773.007 · Cited by 1
Iterative Semi-Supervised Manifold Alignment For Hyperspectral Image Classification
Zhu, L., Ma, L., & Li, X. (2022) · Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing · doi:10.1109/whispers56178.2022.9955059 · Cited by 1
Integration of Discriminate Features and Similarity Preserving for Unsupervised Domain Adaptation
Gilo, O., Mathew, J., & Mondal, S. (2022) · IEEE India Conference · doi:10.1109/indicon56171.2022.10039733 · Cited by 1
Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images
Geiger, M., Martin, D., & Kühl, N. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.103 · Cited by 1
TBFH: A Total-Building-Focused Hybrid Dataset for Remote Sensing Image Building Detection
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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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