计算机科学 ›› 2015, Vol. 42 ›› Issue (10): 7-12.
孟娟,胡谷雨,潘志松,周宇欢
MENG Juan, HU Gu-yu, PAN Zhi-song and ZHOU Yu-huan
摘要： 领域适应学习旨在利用源领域中带标签的样本来解决目标领域的学习问题,其关键在于如何最大化地减小领域间的分布差异,有效解决领域间数据分布的变化。对当前领域适应学习算法进行了归纳和分类,总结了每类算法的特点,分析了5个相关典型算法并比较了其性能。最后指出了领域适应学习值得进一步探索的方向。
| [1] Pan S J,Yang Q.A survey on transfer learning[J].IEEE Transactions on Knowledge and Data Engineering,2010,22(10):1345-1359 [2] 顾鑫,王士同.基于最小包含球的领域迁移学习新方法[J].计算机科学,2013,0(7):187-191 Gu Xin,Wang Shi-tong.Novel domain transfer learning approach using minimum enclosing ball[J].Computer Science,2013,0(7):187-191 [3] Ben-David S,Blitzer J,Crammer K,et al.Analysis of representations for domain adaptation[C]∥Advances in neural information processing systems.2007:137 [4] Blitzer J,McDonald R,Pereira F.Domain adaptation with structural correspondence learning[C]∥Proceedings of the 2006 conference on empirical methods in natural language processing.Association for Computational Linguistics,2006:120-128 [5] Hal Daume III.Frustratingly easy domain adaptation[C]∥Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics.2007:256-263 [6] Satpal S,Sarawagi S.Domain adaptation of conditional probability models via feature subsetting[M]∥Knowledge Discovery in Databases:PKDD 2007.Springer Berlin Heidelberg,2007:224-235 [7] Gong B,Grauman K,Sha F.Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition[J].International Journal of Computer Vision,2014,109(1-2):3-27 [8] Xia R,Zong C,Hu X,et al.Feature ensemble plus sample selection:domain adaptation for sentiment classification[J].Intelligent Systems IEEE,2013,28(3):10-18 [9] Mejova Y,Srinivasan P.Crossing Media Streams with Senti-ment:Domain Adaptation in Blogs,Reviews and Twitter[C]∥ICWSM.2012 [10] Ben-David S,Urner R.On the hardness of domain adaptationand the utility of unlabeled target samples[M]∥Algorithmic Learning Theory.Springer Berlin Heidelberg,2012:139-153 [11] Tao J,Chung F,Wang S.On minimum distribution discrepancy support vector machine for domain adaptation[J].Pattern Reco-gnition,2012,45(11):3962-3984 [12] Bahirat K,Bovolo F,Bruzzone L,et al.A novel domain adaptation Bayesian classifier for updating land-cover maps with class differences in source and target domains[J].IEEE Transactions on Geoscience and Remote Sensing,2012,50(7):2810-2826 [13] Cortes C,Mohri M.Domain adaptation and sample bias correction theory and algorithm for regression[J].Theoretical Computer Science,2014,519:103-126 [14] Japkowicz N,Stephen S.The class imbalance problem:A systematic study[J].Intelligent Data Analysis,2002,6(5):429-449 [15] Shimodaira H.Improving predictive inference under covariateshift by weighting the log-likelihood function[J].Journal of Statistical Planning and Inference,2000,90(2):227-244 [16] Zadrozny B.Learning and evaluating classifiers under sample selection bias[C]∥Proceedings of the twenty-first international conference on Machine learning.ACM,2004:114 [17] Caruana R.Multitask learning[M].Springer US,1998 [18] Zhu X.Semi-supervised learning literature survey[D].University of Wisconsin-Madison,2005 [19] Huang J,Gretton A,Borgwardt K M,et al.Correcting sample selection bias by unlabeled data[C]∥Advances in Neural Information Processing Systems.2006:601-608 [20] Sugiyama M,Nakajima S,Kashima H,et al.Direct importance estimation with model selection and its application to covariate shift adaptation[C]∥Advances in Neural Information Proces-sing Systems.2008:1433-1440 [21] Bickel S,Brückner M,Scheffer T.Discriminative learning under covariate shift[J].The Journal of Machine Learning Research,2009,10:2137-2155 [22] Liao X,Xue Y,Carin L.Logistic regression with an auxiliary data source[C]∥Proceedings of the 22nd International Conference on Machine Learning.ACM,2005:505-512 [23] Lin Y,Lee Y,Wahba G.Support vector machines for classification in nonstandard situations[J].Machine Learning,2002,46(1-3):191-202 [24] Chan Y S,Ng H T.Word Sense Disambiguation with Distribution Estimation[C]∥IJCAI.2005:1010-1015 [25] Chawla N V,Bowyer K W,Hall L O,et al.SMOTE:synthetic minority over-sampling technique[J].Journal of Artifical Intelligence Research,2002,6:321-357 [26] Kubat M,Matwin S.Addressing the curse of imbalanced train-ing sets:one-sided selection[C]∥ICML.1997,97:179-186 [27] Jiang J,Zhai C X.Instance weighting for domain adaptation in NLP[C]∥ACL.2007:264-271 [28] Gao J,Fan W,Jiang J,et al.Knowledge transfer via multiple model local structure mapping[C]∥Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.ACM,2008:283-291 [29] Zhong E,Fan W,Peng J,et al.Cross domain distribution adaptation via kernel mapping[C]∥Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2009:1027-1036 [30] Ren J,Shi X,Fan W,et al.Type-Independent Correction of Sample Selection Bias via Structural Discovery and Re-balancing[C]∥SDM.2008:565-576 [31] Yang J,Yan R,Hauptmann A G.Cross-domain video conceptdetection using adaptive svms[C]∥Proceedings of the 15th international conference on Multimedia.ACM,2007:188-197 [32] Schweikert G,Rtsch G,Widmer C,et al.An empirical analysis of domain adaptation algorithms for genomic sequence analysis[C]∥Advances in Neural Information Processing Systems.2009:1433-1440 [33] Borgwardt K M,Gretton A,Rasch M J,et al.Integrating structured biological data by kernel maximum mean discrepancy[J].Bioinformatics,2006,22(14):e49-e57 [34] Pan S J,Tsang I W,Kwok J T,et al.Domain adaptation viatransfer component analysis[J].IEEE Transactions on Neural Networks,2011,22(2):199-210 [35] Duan L,Xu D,Tsang I W.Domain adaptation from multiplesources:A domain-dependent regularization approach[J].IEEE Transactions on Neural Networks and Learning Systems,2012,23(3):504-518 [36] Duan L,Xu D,Chang S F.Exploiting Web images for event re-cognition in consumer videos:A multiple source domain adaptation approach[C]∥2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE,2012:1338-1345 [37] Chattopadhyay R,Sun Q,Fan W,et al.Multisource domain adap-tation and its application to early detection of fatigue[J].ACM Transactions on Knowledge Discovery from Data (TKDD),2012,6(4):18 [38] Crammer K,Kulesza A,Dredze M.Adaptive regularization ofweight vectors[C]∥Advances in Neural Information Processing Systems.2009:414-422 [39] Luo P,Zhuang F,Xiong H,et al.Transfer learning from multiple source domains via consensus regularization[C]∥Procee-dings of the 17th ACM Conference on Information and Know-ledge Management.ACM,2008:103-112 [40] Mansour Y,Mohri M,Rostamizadeh A.Domain adaptation with multiple sources[C]∥Advances in Neural Information Proces-sing Systems.2009:1041-1048 [41] Shi X,Fan W,Yang Q,et al.Relaxed transfer of different classes via spectral partition[M]∥Machine Learning and Know-ledge Discovery in Databases.Springer Berlin Heidelberg,2009:366-381 [42] Yang J B,Mao Q,Xiang Q L,et al.Domain adaptation for core-ference resolution:An adaptive ensemble approach[C]∥Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.Association for Computational Linguistics,2012:744-753 [43] Duan L,Tsang I W,Xu D,et al.Domain adaptation from multiple sources via auxiliary classifiers[C]∥Proceedings of the 26th Annual International Conference on Machine Learning.ACM,2009:289-296 [44] Belkin M,Niyogi P,Sindhwani V.Manifold regularization:A geo-metric framework for learning from labeled and unlabeled examples[J].Journal of Machine Learning Research,2006,7(3):2399-2434 [45] Evgeniou T,Micchelli C A,Pontil M.Learning multiple taskswith kernel methods[J].Journal of Machine Learning Research,2005,6:615-637 [46] Eaton E,desJardins M.Set-based boosting for instance-leveltransfer[C]∥IEEE International Conference on Data Mining Workshops,2009(ICDMW’09).IEEE,2009:422-428 |
| No related articles found! |
|
||
首页