[1]
|
Pan S J, Tsang I W, Kwok J T, Yang Q. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 2011, 22(2): 199-210[2] Xiang E W, Cao B, Hu D H, Yang Q. Bridging domains using world wide knowledge for transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(6): 770-783[3] Joachims T. Transductive inference for text classification using support vector machines. In: Proceedings of 16th International Conference on Machine Learning (ICML-99). San Francisco, CA: Morgan Kaufmann Publishers, 1999. 200- 209[4] Ozawa S, Roy A, Roussinov D. A multitask learning model for online pattern recognition. IEEE Transactions on Neural Networks, 2009, 20(3): 430-445[5] Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359[6] Bruzzone L, Marconcini M. Domain adaptation problems: a DASVM classification technique and a circular validation strategy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5): 770-787[7] Quanz B, Huan J. Large margin transductive transfer learning. In: Proceedings of the 18th ACM Conference on Information and Knowledge Management (CIKM). New York, USA: ACM 2009. 1327-1336[8] Ben-David S, Blitzer J, Crammer K, Pereira F. Analysis of representations for domain adaptation. In: Proceedings of the Neural Information Processing Systems (NIPS) 2006. Cambridge, MA: MIT Press, 2007[9] Ling X, Dai W Y, Xue G R, Yang Q, Yu Y. Spectral domain-transfer learning. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2008[10] Dai W Y, Xue G R, Yang Q, Yu Y. Co-clustering based classification for out-of-domain documents. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. San Jose, California, USA: ACM, 2007. 210-219[11] Blitzer J, McDonald R, Pereira F. Domain adaptation with structural correspondence learning. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing. Sydney, Australia: Association for Computational Linguistics, 2006. 120-128[12] Blitzer J, Dredze M, Pereira F. Biographies, bollywood, boom-boxes and blenders: domain adaptation for sentiment classification. In: Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics (ACL'07). Prague, CZ: Association for Computational Linguistics, 2007. 440-447[13] Sriperumbudur B K, Gretton A, Fukumizu K, Schlkopf B, Lanckriet G R G. Hilbert space embeddings and metrics on probability measures. Journal of Machine Learning Research, 2010, 11(3): 1517-1561[14] Gretton A, Fukumizu K, Harchaoui Z, Sriperumbudur B K. A fast, consistent kernel two-sample test. In: Proceedings of Advances in Neural Information Processing Systems 22, the 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009). Red Hook, NY: MIT Press, 2010. 673-681[15] Vapnik V N. Statistical Learning Theory. New York: John Wiley and Sons, 1998[16] Phan X H, Nguyen M L, Horiguchi S. Learning to classify short and sparse text web with hidden topics from large-scale data collections. In: Proceedings of the 17th International Conference on World Wide Web (WWW'08). New York, USA: ACM, 2008. 91-100[17] Belkin M, Niyogi P, Sindhwani V, Bartlett P. Manifold regularization: a geometric framework for learning from labeled and unlabeled examples. Journal of Machine Learning Research, 2006, 7(1): 2399-2434[18] Hofmann T, Schlkopf, Smola A J. Kernel methods in machine learning. Annals of Statistics, 2007, 36(3): 1171-1220[19] Sriperumbadur B K, Fukumizu K, Gretton A, Lanckriet G R G, Schlkopf B. Kernel choice and classifiability for RKHS embeddings of probability distributions. In: Advances in Neural Information Processing Systems 22, the 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009). Red Hook, NY: MIT Press, 2010. 1750-1758[20] Smola A, Gretton A, Song L, Schlkopf B. A Hilbert space embedding for distributions. In: Proceedings of the 18th International Conference on Algorithmic Learning Theory. Sendai, Japan: Springer-Verlag, 2007. 13-31[21] Wu Y C, Liu Y F. Robust truncated hinge loss support vector machines. Journal of the American Statistical Association, 2007, 102(479): 974-983[22] Schlkopf B, Herbrich R, Smola A J. A generalized representer theorem. In: Proceedings of the 14th Annual Conference on Computational Learning Theory and 5th European Conference on Computational Learning Theory (COLT'2001). Amsterdam, UK: Springer Press, 2001. 416- 426[23] Kanamori T, Hido S, Sugiyama M. A least-squares approach to direct importance estimation. Journal of Machine Learning Research, 2009, 10(1): 1391-1445[24] Szedmak S, Shawe-Taylor J. Multiclass Learning at One-class Complexity. Technical Report No: 1508, School of Electronics and Computer Science, Southampton, UK, 2005[25] Blitzer J, Crammer K, Kulesza A, Pereira F, Wortman J. Learning bounds for domain adaptation. In: Proceedings of the Neural Information Processing Systems (NIPS) 2006. Cambridge, MA: MIT Press, 2007[26] Gao J, Fan W, Jiang J, Han J W. Knowledge transfer via multiple model local structure mapping. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA: ACM, 2008[27] Gao Jun, Wang Shi-Tong, Deng Zhao-Hong. Global and local preserving based semi-supervised support vector machine. Acta Electronica Sinica, 2010, 38(7): 1626-1633 (皋军, 王士同, 邓赵红. 基于全局和局部保持的半监督支持向量机. 电子学报, 2010, 38(7): 1626-1633)[28] Cai D, He X F, Han J W, Zhang H J. Orthogonal Laplacianfaces for face recognition. IEEE Transactions on Image Processing, 2006, 15(11): 3608-3614[29] Chang C C, Lin C J. LIBSVM: a library for support vector machines. Science, 2001, 2(3): 1-39[30] Beitzel S M, Jensen E C, Frieder O, Lewis D D, Chowdhury A, Kolcz A. Improving automatic query classification via semi-supervised learning. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM'05). Washington DC, USA: IEEE Computer Society, 2005. 42-49
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