Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Benchmarking Least Squares Support Vector Machine Classifiers
Machine Learning
Improving SVM accuracy by training on auxiliary data sources
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning Multiple Tasks with Kernel Methods
The Journal of Machine Learning Research
Beyond the point cloud: from transductive to semi-supervised learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
The Journal of Machine Learning Research
Cross-domain video concept detection using adaptive svms
Proceedings of the 15th international conference on Multimedia
Learning from Multiple Sources
The Journal of Machine Learning Research
Transfer learning from multiple source domains via consensus regularization
Proceedings of the 17th ACM conference on Information and knowledge management
Domain adaptation with structural correspondence learning
EMNLP '06 Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing
Using large-scale web data to facilitate textual query based retrieval of consumer photos
MM '09 Proceedings of the 17th ACM international conference on Multimedia
Frustratingly easy semi-supervised domain adaptation
DANLP 2010 Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing
Predictive distribution matching SVM for multi-domain learning
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Multi-source domain adaptation and its application to early detection of fatigue
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Domain selection and adaptation in smart homes
ICOST'11 Proceedings of the 9th international conference on Toward useful services for elderly and people with disabilities: smart homes and health telematics
A pseudo relevance feedback based cross domain video concept detection
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
Semi-supervised multi-task learning of structured prediction models for web information extraction
Proceedings of the 20th ACM international conference on Information and knowledge management
On minimum distribution discrepancy support vector machine for domain adaptation
Pattern Recognition
Heterogeneous domain adaptation using manifold alignment
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Source-selection-free transfer learning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Multisource domain adaptation and its application to early detection of fatigue
ACM Transactions on Knowledge Discovery from Data (TKDD) - Special Issue on the Best of SIGKDD 2011
Discovering latent domains for multisource domain adaptation
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part II
Image annotation by semi-supervised cross-domain learning with group sparsity
Journal of Visual Communication and Image Representation
OMS-TL: a framework of online multiple source transfer learning
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
An Ensemble Model for Mobile Device based Arrhythmia Detection
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics
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We propose a multiple source domain adaptation method, referred to as Domain Adaptation Machine (DAM), to learn a robust decision function (referred to as target classifier) for label prediction of patterns from the target domain by leveraging a set of pre-computed classifiers (referred to as auxiliary/source classifiers) independently learned with the labeled patterns from multiple source domains. We introduce a new data-dependent regularizer based on smoothness assumption into Least-Squares SVM (LS-SVM), which enforces that the target classifier shares similar decision values with the auxiliary classifiers from relevant source domains on the unlabeled patterns of the target domain. In addition, we employ a sparsity regularizer to learn a sparse target classifier. Comprehensive experiments on the challenging TRECVID 2005 corpus demonstrate that DAM outperforms the existing multiple source domain adaptation methods for video concept detection in terms of effectiveness and efficiency.