R. Ando and T. Zhang, A framework for learning predictive structures from multiple tasks and unlabeled data, Journal of Machine Learning Research, vol.6, pp.1817-1853, 2005.

S. Ayache and G. Quénot, Video Corpus Annotation Using Active Learning, Proceedings of the 30th European Conference on Information Retrieval Research (ECIR), pp.187-198, 2008.
DOI : 10.1007/978-3-540-78646-7_19
URL : https://hal.archives-ouvertes.fr/hal-01089795

S. Ayache, G. Quénot, and J. Gensel, Image and video indexing using networks of operators, Journal on Image and Video Processing, pp.1-1, 2007.
URL : https://hal.archives-ouvertes.fr/hal-00953803

M. T. Bahadori, Y. Liu, and D. Zhang, Learning with Minimum Supervision: A General Framework for Transductive Transfer Learning, 2011 IEEE 11th International Conference on Data Mining, pp.61-70, 2011.
DOI : 10.1109/ICDM.2011.92

M. Balcan, A. Blum, and N. Srebro, Improved guarantees for learning via similarity functions, Proceedings of the Annual Conference on Computational Learning Theory (COLT), pp.287-298, 2008.

M. Balcan, A. Blum, and N. Srebro, A theory of learning with similarity functions, Machine Learning, vol.27, issue.11, pp.89-112, 2008.
DOI : 10.1007/s10994-008-5059-5

A. Bellet, A. Habrard, and M. Sebban, Learning Good Edit Similarities with Generalization Guarantees, Proceedings of European Conference on Machine Learning and Principles of Data Mining and Knowledge Discovery, pp.188-203, 2011.
DOI : 10.1007/978-3-642-23780-5_22
URL : https://hal.archives-ouvertes.fr/hal-00608631

S. Ben-david, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira et al., A theory of learning from different domains, Machine Learning, vol.60, issue.1-2, pp.151-175, 2010.
DOI : 10.1007/s10994-009-5152-4

S. Ben-david, J. Blitzer, K. Crammer, and F. Pereira, Analysis of representations for domain adaptation, Proceedings of Advances in Neural Information Processing Systems (NIPS), pp.137-144, 2007.

S. Ben-david, T. Lu, T. Luu, and D. Pal, Impossibility theorems for domain adaptation, pp.129-136, 2010.

A. Bergamo and L. Torresani, Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach, Proceedings of Advances in Neural Information Processing Systems (NIPS), 2010.

J. Blitzer, D. Foster, and S. Kakade, Domain adaptation with coupled subspaces, Proceedings of AISTATS', 2011.

L. Bruzzone and M. Marconcini, Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.32, issue.5, pp.770-787, 2010.
DOI : 10.1109/TPAMI.2009.57

C. Chang, C. Lin, R. Chattopadhyay, J. Ye, S. Panchanathan et al., LIBSVM, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.717-725, 2001.
DOI : 10.1145/1961189.1961199

M. Chen, K. Weinberger, and J. Blitzer, Co-training for domain adaptation, Proceedings of Advances in Neural Information Processing Systems (NIPS), 2011.

C. Cortes and M. Mohri, Domain Adaptation in Regression, Proceedings of International Conference on Algorithmic Learning Theory (ALT), pp.308-323, 2011.
DOI : 10.1007/978-3-642-24412-4_25

I. Daumé and H. , Frustratingly easy domain adaptation, Proceedings of the Association for Computational Linguistics (ACL), 2007.

I. Daumé, H. Kumar, A. Saha, and A. , Co-regularization based semi-supervised domain adaptation, Proceedings of Advances in Neural Information Processing Systems (NIPS), 2010.

L. Duan, I. Tsang, D. Xu, and T. Chua, Domain adaptation from multiple sources via auxiliary classifiers, Proceedings of the 26th Annual International Conference on Machine Learning, ICML '09, p.37, 2009.
DOI : 10.1145/1553374.1553411

H. Fei and J. Huan, Structured feature selection and task relationship inference for multi-task learning, Proceedings of the 11th IEEE International Conference on Data Mining (ICDM)', IEEE, pp.171-180, 2011.

R. Freund, Polynomial-time algorithms for linear programming based only on primal scaling and projected gradients of a potential function, Mathematical Programming 51, pp.203-222, 1991.
DOI : 10.1007/BF01586933

B. Geng, D. Tao, and C. Xu, DAML: Domain Adaptation Metric Learning, IEEE Transactions on Image Processing, vol.20, issue.10, pp.2980-2989, 2011.
DOI : 10.1109/TIP.2011.2134107

P. Guerra, A. Veloso, W. M. Jr, and V. Almeida, From bias to opinion: a transfer-learning approach to real-time sentiment analysis, Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)', ACM, pp.150-158, 2011.

J. Huang, A. Smola, A. Gretton, K. Borgwardt, and B. Schölkopf, Correcting sample selection bias by unlabeled data, Proceedings of Advances in Neural Information Processing Systems (NIPS), pp.601-608, 2006.

J. Jiang, A literature survey on domain adaptation of statistical classifiers, 2008.

J. Jiang and C. Zhai, Instance weighting for domain adaptation in nlp, Proceedings of the Association for Computational Linguistics (ACL), 2007.

T. Joachims, Transductive inference for text classification using support vector machines, Proceedings of International Conference on Machine Learning (ICML), pp.200-209, 1999.

K. Junejo and A. Karim, Robust personalizable spam filtering via local and global discrimination modeling, Knowledge and Information Systems, vol.24, issue.4, pp.1-36, 2012.
DOI : 10.1007/s10115-012-0477-x

B. Kulis, K. Saenko, and T. Darrell, What you saw is not what you get: Domain adaptation using asymmetric kernel transforms, CVPR 2011, pp.1785-1792, 2011.
DOI : 10.1109/CVPR.2011.5995702

J. Macqueen, Some methods of classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability', pp.281-297, 1967.

Y. Mansour, M. Mohri, and A. Rostamizadeh, Domain adaptation with multiple sources, Proceedings of Advances in Neural Information Processing Systems (NIPS), pp.1041-1048, 2008.

Y. Mansour, M. Mohri, and A. Rostamizadeh, Domain adaptation: Learning bounds and algorithms, Proceedings of Annual Conference on Learning Theory (COLT), pp.19-30, 2009.

S. Pan and Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359, 2010.
DOI : 10.1109/TKDE.2009.191

G. Schweikert, C. Widmer, B. Schölkopf, and G. Rätsch, An empirical analysis of domain adaptation algorithms for genomic sequence analysis, Proceedings of Advances in Neural Information Processing Systems (NIPS), pp.1433-1440, 2008.

C. Seah, I. Tsang, Y. Ong, and K. Lee, Predictive Distribution Matching SVM for Multi-domain Learning, Proceedings of European Conference on Machine Learning and Principles of Data Mining and Knowledge Discovery, pp.231-247, 2010.
DOI : 10.1007/978-3-642-15880-3_21
URL : http://c2inet.sce.ntu.edu.sg/ivor/publication/ecml2010.pdf

A. Smeaton, P. Over, and W. Kraaij, High-Level Feature Detection from Video in TRECVid: a 5-Year Retrospective of Achievements, in 'Multimedia Content Analysis, Theory and Applications, pp.151-174, 2009.

M. Sugiyama, S. Nakajima, H. Kashima, P. Von-bünau, and M. Kawanabe, Direct importance estimation with model selection and its application to covariate shift adaptation, Proceedings of Advances in Neural Information Processing Systems (NIPS), 2007.

B. Wang, J. Tang, W. Fan, S. Chen, C. Tan et al., Query-dependent cross-domain ranking in heterogeneous network, Knowledge and Information Systems, vol.23, issue.1, pp.1-37, 2012.
DOI : 10.1007/s10115-011-0472-7

H. Xu and S. Mannor, Robustness and generalization, Proceedings of Annual Conference on Computational Theory (COLT), pp.503-515, 2010.
DOI : 10.1007/s10994-011-5268-1

H. Xu and S. Mannor, Robustness and generalization, Machine Learning, vol.75, issue.7, pp.391-423, 2012.
DOI : 10.1007/s10994-011-5268-1

Z. Xu and K. Kersting, Multi-task Learning with Task Relations, 2011 IEEE 11th International Conference on Data Mining, pp.884-893, 2011.
DOI : 10.1109/ICDM.2011.108

G. Xue, W. Dai, Q. Yang, and Y. Yu, Topic-bridged PLSA for cross-domain text classification, Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pp.627-634, 2008.
DOI : 10.1145/1390334.1390441

Y. Ye, An O(n 3 L) potential reduction algorithm for linear programming, Mathematical Programming, vol.47, issue.1-3, pp.239-258, 1991.
DOI : 10.1007/BF01594937

Y. Zhang and D. Yeung, Transfer metric learning by learning task relationships, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '10, pp.1199-1208, 2010.
DOI : 10.1145/1835804.1835954
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.174.9098