[1]
|
Pan S J, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359
|
[2]
|
Quanz B, Huan J. Large margin transductive transfer learning. In: Proceedings of the 18th ACM conference on Information and knowledge management. New York, USA: ACM, 2009. 1327-1336
|
[3]
|
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
|
[4]
|
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
|
[5]
|
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
|
[6]
|
Ben-David S, Blitzer J, Crammer K, Pereira F. Analysis of representations for domain adaptation. In: Proceedings of the 2007 Advances in Neural Information Processing Systems 19. Cambridge: MIT Press, 2007. 137-144
|
[7]
|
Daumé H III, Marcu D. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 2006, 26(1): 101-126
|
[8]
|
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, NY, USA: ACM, 2008. 488-496
|
[9]
|
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. New York, USA: ACM, 2007: 210-219
|
[10]
|
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 for Computational Linguistics. Prague, Czekh Republic: Association for Computational Linguistics, 2007. 440-447
|
[11]
|
Sriperumbudur B K, Gretton A, Fukumizu K, Schölkopf, Lanckriet G R G. Hilbert space embeddings and metrics on probability measures. Journal of Machine Learning Research, 2010, 11: 1517-1561
|
[12]
|
Sriperumbudur B K, Fukumizu K, Gretton A, Lanckriet G R G, Schölkopf. Kernel choice and classifiability for RKHS embeddings of probability distributions. In: Proceedings of the 2009 Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2009, 22: 1750-1758
|
[13]
|
Gretton A, Fukumizu K, Harchaoui Z, Sriperumbudur B K. A fast, consistent kernel two-sample test. In: Proceedings of the 2010 Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2010. 673-681
|
[14]
|
Vapnik V N. The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995. 123-167
|
[15]
|
Pal M, Foody G M. Feature selection for classification of hyper spectral data by SVM. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(5): 2297-2307
|
[16]
|
Liu Jian-Wei, Li Shuang-Cheng, Luo Xiong-Lin. Classification algorithm of support vector machine via p-norm regularization. Acta Automatica Sinica, 2012, 38(1): 76-87(刘建伟, 李双成, 罗雄麟. p范数正则化支持向量机分类算法. 自动化学报, 2012, 38(1): 76-87)
|
[17]
|
Liu Qiao, Qin Zhi-Guang, Chen Wei, Zhang Feng-Li. Zero-norm penalized feature selection support vector machine. Acta Automatica Sinica, 2011, 37(2): 252-256(刘峤, 秦志光, 陈伟, 张凤荔. 基于零范数特征选择的支持向量机模型. 自动化学报, 2011, 37(2): 252-256)
|
[18]
|
Hu Wen-Jun, Wang Shi-Tong. Fast real-time decision approach of support vector data description. Acta Automatica Sinica, 2011, 37(9): 1085-1094(胡文军, 王士同. SVDD的快速实时决策方法. 自动化学报, 2011, 37(9): 1085-1094)
|
[19]
|
Wang Xiao-Ming, Wang Shi-Tong. Theoretical analysis for the optimization problem of support vector data description. Journal of Software, 2011, 22(7): 1551-1560(王晓明, 王士同. 支撑向量数据域描述优化问题最优解理论分析. 软件学报, 2011, 22(7): 1551-1560)
|
[20]
|
Duan L X, Xu D, Tsang I W H. Domain adaptation from multiple sources: a domain-dependent regularization approach. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(3): 504-518
|
[21]
|
Duan L X, Tsang I W, Xu D, Chua T S. Domain adaptation from multiple sources via auxiliary classifiers. In: Proceedings of the 26th Annual International Conference on Machine Learning. New York, NY, USA: ACM, 2009. 289-296
|
[22]
|
Li S S, Zong C Q. Multi-domain adaptation for sentiment classification: using multiple classifier combining methods. In: Proceedings of the 2008 International Conference on Natural Language Processing and Knowledge Engineering. Beijing, China: IEEE, 2008. 1-8
|
[23]
|
Yang J, Yan R, Hauptmann A G. Cross-domain video concept detection using adaptive SVMs. In: Proceedings of the 15th International Conference on Multimedia. New York, NY: ACM, 2007. 188-197
|
[24]
|
Vapnik V N. Statistical Learning Theory. New York: John Wiley and Sons, 1998
|
[25]
|
Hofmann T, Schölkopf B, Smola A J. Kernel methods in machine learning. Annals of Statistics, 2007, 36(3): 1171-1220
|
[26]
|
Kim J, Scott C D. L2 kernel classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(10): 1822-1831
|
[27]
|
Schölkopf 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). London, UK: Springer-Verlag, 2001. 416-426
|
[28]
|
Dai Y H, Chen H C, Peng T. Cost-sensitive support vector machine based on weighted attribute. In: Proceedings of the 2009 International Forum on Information Technology and Applications. Washington, DC: IEEE, 2009: 690-692
|
[29]
|
Joshi M V. On evaluating performance of classifiers for rare classes. In: Proceedings of the 2nd IEEE International Conference on Data Mining. Maebashi City, Japan, 2002. 641-644
|