[1]
|
Pan J L, Yang Q. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(10): 1345-1359
|
[2]
|
Duan L X, Tsang I W, Xu D. Domains transfer multiple kernel learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 465-479
|
[3]
|
Tu W T, Sun S L. A subject transfer framework for EEG classification. Neurocomputing, 2012, 82: 109-116
|
[4]
|
Daume Ⅲ H, Marcu D. Domain adaptation for statistical classifiers. Journal of Artificial Intelligence Research, 2006, 26(1): 101-126
|
[5]
|
Biekel S, Bruckner M, Scheffer T. Discriminative learning for differing training and test distributions. In: Proceedings of the 24th International Conference on Machine Learning. New York, USA: ACM, 2007. 81-88
|
[6]
|
Bickel S, Sawade C, Scheffer T. Transfer learning by distribution matching for targeted advertising. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. Cambridge: MIT Press, 2009. 145-152
|
[7]
|
Wu P C, Dietterich T G. Improving SVM accuracy by training on auxiliary data sources. In: Proceedings of the 21st International Conference on Machine Learning (ICML). New York, USA: ACM, 2004. 110-117
|
[8]
|
Dai W Y, Yang Q, Xue G R, Yu Y. Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning (ICML). New York, USA: ACM, 2007. 193-200
|
[9]
|
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
|
[10]
|
Xu Z J, Sun S L. Multi-view transfer learning with Adaboost. In: Proceedings of the 23rd Conference on Tools with Artificial Intelligence. Boca Raton, FL: IEEE, 2011. 399-402
|
[11]
|
Chen M M, Weinberger K Q, Blitzer J. Co-training for domain adaptation. In: Proceedings of the 25th Conference on Neural Information Processing Systems (NIPS). New York: USA: Curran Associates, Inc., 2011. 1231-1240
|
[12]
|
Xu Z J, Sun S L. Multi-source transfer learning with multi-view adaboost. Neural Information Processing, 2012, 7665: 332-339
|
[13]
|
Jiang Yi-Zhang, Deng Zhao-Hong, Wang Shi-Tong. Mamdani-Larsen type transfer learning fuzzy system. Acta Automatica Sinica, 2012, 38(9): 1393-1409 (蒋亦樟, 邓赵红, 王士同. ML型迁移学习模糊系统. 自动化学报, 2012, 38(9): 1393-1409)
|
[14]
|
Zhu Mei-Qiang, Cheng Yu-Hu, Li Ming, Wang Xue-Song, Feng Huan-Ting. A hybrid transfer algorithm for reinforcement learning based on spectral method. Acta Automatica Sinica, 2012, 38(11): 1765-1776 (朱美强, 程玉虎, 李明, 王雪松, 冯涣婷. 一类基于谱方法的强化学习混合迁移算法. 自动化学报, 2012, 38(11): 1765-1776)
|
[15]
|
Jiang W H, Chung F L. Transfer spectral clustering. In: Proceedings of the 2012 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD). Berlin, Heidelberg: Springer-Verlag, 2012. 789-803
|
[16]
|
Argyriou A, Micchelli C A, Pontil M, Ying Y M. A spectral regularization frame work for multi-task structure learning. In: Proceedings of Advances in Neural Information Processing Systems (NIPS 2008). Cambridge, MA: MIT Press, 2007. 25-32
|
[17]
|
Pan S J L, Kwok J T, Yang Q. Transfer learning via dimensionality reduction. In: Proceedings of the 23rd International Conference on Artificial Intelligence. California, USA: AAAI Press, 2008. 677-682
|
[18]
|
Pan S J L, Ni X C, Sun J T, Yang Q, Chen Z. Cross-domain sentiment classification via spectral feature alignment. In: Proceedings of the 19th International Conference on World Wide Web (WWW'10). New York, USA: ACM, 2010. 751-760
|
[19]
|
Tu W T, Sun S L. Transferable discriminative dimensionality reduction. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence (CTAI). Boca Raton, FL: IEEE, 2011. 865-868
|
[20]
|
Gao X B, Wang X M, Li X L, Tao D C. Transfer latent variable model based on divergence analysis. Pattern Recognition, 2011, 44(10-11): 2358-2366
|
[21]
|
Gao X B, Wang Z, Yan P K, Li X L. Transfer learning for pedestrian detection. Neurocomputing, 2013, 100: 51-57
|
[22]
|
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 (NIPS 2010). Red Hook, NY: MIT Press, 2010. 673-681
|
[23]
|
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)
|
[24]
|
Deng Nai-Yang, Tian Ying-Jie. New Method in Data Mining: Support Vector Machine. Beijing: Science Press, 2004. 5-16 (邓乃阳, 田英杰. 数据挖掘中的新方法: 支持向量机. 北京: 科学出版社, 2004. 5-16)
|
[25]
|
Yuan Ya-Xiang. Optimization Theory and Methods. Beijing: Science Press, 1997. 176-189 (袁亚湘. 最优化理论与方法. 北京: 科学出版社, 1997. 176-189)
|
[26]
|
Tao Jian-Wen, Wang Shi-Tong. Kernel support vector machine for domain adaptation. Acta Automatica Sinica, 2012, 38(5): 797-881 (陶剑文, 王士同. 领域适应核支持向量机. 自动化学报, 2012, 38(5): 797-881)
|
[27]
|
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
|
[28]
|
Zhang J Y, Zhang B, Jiang X Z. Analyses of feature extraction methods based on wavelet transform. Signal Processing, 2000, 16(2): 156-162
|