域自适应学习研究进展

刘建伟; 孙正康; 罗雄麟

doi:10.3724/SP.J.1004.2014.01576

域自适应学习研究进展

doi: 10.3724/SP.J.1004.2014.01576

1.
中国石油大学(北京)自动化研究所北京 102249

基金项目:

国家重点基础研究发展计划（973计划）（2012CB720500），国家自然科学基金（21006127），中国石油大学（北京）基础学科研究基金（JCXK-2011-07）资助

详细信息

作者简介:
孙正康中国石油大学（北京）地球物理与信息工程学院硕士研究生. 主要研究方向为机器学习，域自适应学习研究.E-mail：sunzhengkang@126.com

通讯作者:
刘建伟博士，中国石油大学（北京）副研究员. 主要研究方向为智能信息处理，机器学习，复杂系统分析，预测与控制，算法分析与设计.E-mail：liujw@cup.edu.cn

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出版历程
- 收稿日期: 2013-09-22
- 修回日期: 2013-12-31
- 刊出日期: 2014-08-20

Review and Research Development on Domain Adaptation Learning

1.
Research Institute of Automation, China University of Petroleum, Beijing 102249

Funds:

Supported by National Basic Research Program of China (973 Program) (2012CB720500), National Natural Science Foundation of China (21006127), and Basic Subject Research Fund of China University of Petroleum (JCXK-2011-07)

摘要

摘要: 传统的机器学习假设测试样本和训练样本来自同一概率分布. 但当前很多学习场景下训练样本和测试样本可能来自不同的概率分布. 域自适应学习能够有效地解决训练样本和测试样本概率分布不一致的学习问题，作为机器学习新出现的研究领域在近几年受到了广泛的关注. 鉴于域自适应学习技术的重要性，综述了域自适应学习的研究进展. 首先概述了域自适应学习的基本问题，并总结了近几年出现的重要的域自适应学习方法. 接着介绍了近几年提出的较为经典的域自适应学习理论和当下域自适应学习的热门研究方向，包括样例加权域自适应学习、特征表示域自适应学习、参数和特征分解域自适应学习和多源域自适应学习. 然后对域自适应学习进行了相关的理论分析，讨论了高效的度量判据，并给出了相应的误差界. 接着对当前域自适应学习在算法、模型结构和实际应用这三个方面的研究新进展进行了综述. 最后分别探讨了域自适应学习在特征变换和假设、训练优化、模型和数据表示、NLP 研究中存在的问题这四个方面的有待进一步解决的问题.
- 域自适应学习 /
- 迁移假设 /
- 单源域--单目标域 /
- 多源域--多目标域 /
- 域自适应学习理论
Abstract: Traditional supervised learning algorithms assume that the training data and the test data are drawn from the same probability distribution. But in many cases, this assumption is too simplified, and too harsh in light of modern applications of machine learning. Domain adaptation approaches are used to solve the problem that arises when the data distribution in the test domain is different from that in the training domain. Although the domain adaptation problem is a fundamental problem in machine learning, it only started gaining much attention very recently. In view of the theoretical and practical significance of domain adaptation methods, this paper summarizes the learning algorithm for domain adaptation. Firstly, the basic issues of domain adaptation and several important methods on domain adaptation are summarizes. Next, learning theory and hot research direction on domain adaptation are described, including instance weighting based method, feature representation based method, parameter and feature decomposition based method, domain adaptation with multiple sources. Thirdly, the theoretical analysis for domain adaptation and the effective distribution metric learning are illustrated. At the same time, the error bounds of those algorithms are also presented. Fourthly, new research and development in three aspects on domain adaptation in recent years are reviewed, including learning algorithm, model structure and practical application. Finally, the problems to be solved in aspects of feature transform and assumption, optimization algorithm, data representation and model, and the problem to be solved in NLP are discussed.
- Domain adaptation learning /
- transfer hypothesis /
- single source-domain single target-domain /
- multiple sources-domain multiple targets-domain /
- domain adaptation learning theory

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参考文献(103)

[1]	[2] Mansour Y, Mohri M, Rostamizadeh A. Domain adaptation: learning bounds and algorithms. In: Proceedings of the 22nd Annual Conference on Learning Theory. Montral, Canada: Omnipress, 2009. 34-47
[2]	[3] Blitzer J, Crammer K, Kulesza A. Learning bounds for domain adaptation. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates, 2007. 129-136
[3]	[4] Cortes C, Mansour Y, Mohri M. Learning bounds for importance weighting. In: Proceedings of the 24th Annual Conference on Neural Information Processing Systems. Vancouver, Canada: Curran Associates, 2010. 442-450
[4]	[5] Zhong E, Fan W, Peng J, Zhang K, Ren J T, Turaga D S, Verscheure O. Cross domain distribution adaptation via kernel mapping. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Paris, France: ACM, 2009. 1027-1036
[5]	[6] Kulis B, Saenko K, Darrell T. What you saw is not what you get: domain adaptation using asymmetric kernel transforms. In: Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition. Colorado Springs, CO, USA: IEEE, 2011. 1785-1792
[6]	[7] Ben-David S, Blitzer J, Crammer K, Kulesza A, Pereira F, Vauqhan J W. A theory of learning from different domains. Machine Learning, 2010, 79(1-2): 151-175
[7]	[8] Joshi M, Dredze M, Cohen W W. What's in a domain? multi-domain learning for multi-attribute data. In: Proceedings of the 2013 NAACL-HLT. Westin Peachtree Plaza Hotel, Atlanta, Georgia, USA: The Association for Computational Linguistics, 2013. 685-690
[8]	[9] Wan X J. Co-training for cross-lingual sentiment classification. In: Proceedings of the 2009 Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP. Singapore: ACL, 2009. 235-243
[9]	Gabrilovich E, Markovitch S. Feature generation for text categorization using world knowledge. In: Proceedings of the 19th International Joint Conference on Artificial Intelligence. Edinburgh, Scotland, UK: Professional Book Center, 2005. 1048-1053
[10]	Cucerzan S. Large-scale named entity disambiguation based on Wikipedia data. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Prague, Czech Republic: ACL, 2007. 708-716
[11]	Sarinnapakorn K, Kubat M. Combining subclassifiers in text categorization: a DST-based solution and a case study. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(12): 1638-1651
[12]	Gabriel P C F, Jeffrey X Y, Lu H J, Philip S Y. Text classification without negative examples revisit. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(1): 6-20
[13]	Al-Mubaid H, Umair S A. A new text categorization technique using distributional clustering and learning logic. IEEE Transactions on Knowledge and Data Engineering, 2006, 18(9): 1156-1165
[14]	Gabrilovich E, Markovitch S. Enhancing text categorization with encyclopedic knowledge. In: Proceedings of the 21st National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference. 2006, Boston, Massachusetts, USA: AAAI Press, 2006. 1301-1306
[15]	Joshi M, Cohen W W, Dredze M, Ros C P. Multi-domain learning: when do domains matter? In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Jeju, Island, Korea: Association for Computational Linguistics, 2012. 1302-1312
[16]	Pan S J, Kwok J T, Yang Q, Pan J J. Adaptive localization in a dynamic WiFi environment through multi-view learning. In: Proceedings of the 22nd AAAI Conference on Artificial Intelligence. Vancouver, British Columbia, Canada: AAAI Press, 2007. 1023-1027
[17]	Chen X Y, Yuan X T, Chen Q, Yan S C, Chua T S. Multi-label visual classification with label exclusive context. IEEE Transactions on Computer Vision, 2011, 1(1): 834-841
[18]	Tu W T, Sun S L. Transferable discriminative dimensionality reduction. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence. Boca Raton, FL, USA: IEEE, 2011. 865-868
[19]	Cortes C, Mohri M. Domain adaptation in regression. In: Proceedings of the 22nd Algorithmic Learning Theory International Conference. Espoo, Finland: Springer, 2011. 308-323
[20]	Zadrozny B. Learning and evaluating classifiers under sample selection bias. In: Proceedings of the 21st International Conference on Machine Learning. New York, NY, USA: ACM, 2004. 114-115
[21]	Sugiyama M, Nakajima S, Kashima H. Direct importance estimation with model selection and its application to covariate shift adaptation. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates, 2008. 1433-1440
[22]	Fan W, Davidson I, Zadrozny B, Yu P S. An improved categorization of classifier's sensitivity on sample selection bias data mining. In: Proceedings of the 5th IEEE International Conference on Data Mining. Houston, Texas, USA: IEEE, 2005. 27-30
[23]	Quionero-Candela J, Sugiyama M, Schwaighofer A. Dataset Shift in Machine Learning. Boston: The MIT Press, 2009
[24]	Dai W Y, Yang Q, Xue G R, Yu Y. Boosting for transfer learning. In: Proceedings of the 24th International Conference on Machine Learning. Beijing, China: ACM, 2007. 193-200
[25]	Xu Z J, Sun S L. Multi-view transfer learning with Adaboost. In: Proceedings of the 23rd IEEE International Conference on Tools with Artificial Intelligence. Boca Raton, FL, USA: IEEE, 2011. 399-402
[26]	Xu Z J, Sun S L. Multi-source transfer learning with multi-view Adaboost. In: Proceedings of the 19th International Conference on Neural Information Processing. Doha, Qatar: Springer, 2012. 332-339
[27]	Tao J W, Chung K F L, Wang S. On minimum distribution discrepancy support vector machine for domain adaptation. Pattern Recognition, 2012, 45(11): 3962-3984
[28]	Sun S L, Xu Z J, Yang M. Transfer learning with part-based ensembles. Multiple Classifier Systems, 2013, 7872: 271-282
[29]	Tu W T, Sun S L. Cross-domain representation-learning framework with combination of class-separate and domain-merge objectives. In: Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining. Beijing, China: ACM, 2012. 18-25
[30]	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: ALC, 2006. 120-128
[31]	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 Press, 2007. 210-219
[32]	Pan S J, Kwok J T, Yang Q. Transfer learning via dimensionality reduction. In: Proceedings of the 23rd AAAI Conference on Artificial Intelligence. Chicago, Illinois, USA: AAAI Press, 2008. 677-682
[33]	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
[34]	Daum III H. Frustratingly easy domain adaptation. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics. Prague, Czech Republic: ACL, 2007. 1785-1787
[35]	Bonilla E, Chai K M, Williams C. Multi-task Gaussian process prediction. In: Proceedings of the 21st Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates, 2008. 153-160
[36]	Schwaighofer A, Tresp V, Yu K. Learning Gaussian process kernels via hierarchical Bayes. In: Proceedings of the 17th Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: MIT Press, 2004. 1209-1216
[37]	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. Las Vegas, Nevada, USA: ACM Press, 2008. 283-291
[38]	Tu W T, Sun S L. Dynamical ensemble learning with model-friendly classifiers for domain adaptation. In: Proceedings of the 21st International Conference on Pattern Recognition. Tsukuba, Japan: IEEE, 2012. 1181-1184
[39]	Ben-David S, Lu T, Luu T. Impossibility theorems for domain adaptation. Journal of Machine Learning Research, 2010, 9: 129-136
[40]	Ben-David S, Urner R. On the hardness of domain adaptation. In: Proceedings of the 23rd International Conference. Lyon, France: Springer, 2012. 139-153
[41]	Ben-David S, Shalev-Shwartz S, Urner R. Domain adaptationcan quantity compensate for quality? In: Proceedings of the 2012 International Symposium on Artificial Intelligence and Mathematics. Fort Lauderdale, Florida, USA: ISAIM, 2012. 641-648
[42]	Xu H, Mannor S. Robustness and generalization. Machine Learning, 2012, 86(3): 391-423
[43]	Mansour Y, Schain M. Robust domain adaptation. In: Proceedings of the 12th International Symposium on Artificial Intelligence and Mathematics. Florida, USA: ISAIM, 2012, 27-36
[44]	Mansour Y, Mohri M, Rostamizadeh A. Domain adaptation with multiple sources. In Proceedings of the 22nd Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates, 2008. 1041-1048
[45]	Duan L X, Xu D, Tsang I W. Domain adaptation from multiple sources: a domain-dependent regularization approach. IEEE Transactions on Neural Networks and Learning Systems, 2012, 23(3): 504-518
[46]	Bellet A, Habrard A, Sebban M. Similarity learning for provably accurate sparse linear classification. In: Proceedings of the 29th International Conference on Machine Learning. Edinburgh, Scotland, UK: Omnipress, 2012. 1871-1878
[47]	Morvant E, Habrard A, Ayache S. Parsimonious unsupervised and semi-supervised domain adaptation with good similarity functions. Knowledge and Information Systems, 2012, 33(2): 309-349
[48]	Chapelle O, Shivaswamy P, Vadrevu S, Weinberger K, Zhang Y, Tseng B. Boosted multi-task learning. Machine Learning, 2011, 85(1-2): 149-173
[49]	Sun A, Grishman R. Cross-domain bootstrapping for named entity recognition. In: Proceedings of the SIGIR 2011 Workshop on Entity-Oriented Search. Beijing, China: ACM Press, 2011
[50]	Novotney S, Schwartz R M, Khudanpur S. Unsupervised arabic dialect adaptation with self-training. Interspeech, 2011, 541-544
[51]	Zhuang F Z, Luo P, Xiong H, He Q, Xiong Y H, Shi Z Z. Exploiting associations between word clusters and document classes for cross-domain text categorization. Statistical Analysis and Data Mining, 2011, 4(1): 100-114
[52]	Prettenhofer P, Stein B. Cross-language text classification using structural correspondence learning. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics. Uppsala, Sweden: ALC, 2010. 1118-1127
[53]	Blitzer J, Kakade S, Foster D P. Domain adaptation with coupled subspaces. Journal of Machine Learning Research, 2011, 15(11): 173-181
[54]	Chen M, Weinberger K Q, Blitzer J. Co-training for domain adaptation. In: Proceedings of the 25th Annual Conference on Neural Information Processing Systems. Granada, Spain: Springer, 2011. 2456-2464
[55]	Tao J W, Chung F L, Wang S T. A kernel learning framework for domain adaptation learning. Science China Information Sciences, 2012, 55(9): 1983-2007
[56]	Scholkopf B, Smola A J, Williamson R C, Bartlett P L. New support vector algorithms. Neural computation, 2000, 12(5): 1207-1245
[57]	Joachims T. Transductive inference for text classification using support vector machines. In: Proceedings of the 16th International Conference on Machine Learning. Bled, Slovenia: Morgan Kaufmann, 1999. 200-209
[58]	Suykens J A K, Vandewalle J. Least squares support vector machine classifiers. Neural Processing Letters, 1999, 9(3): 293-300
[59]	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
[60]	Duan L X, Tsang I W, Xu D, Maybank S J. Domain transfer svm for video concept detection. In: Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Miami, Florida, USA: IEEE, 2009. 1375-1381
[61]	Gretton A, Fukumizu K, Sriperumbudur B K. A fast consistent kernel two-sample test. In: Proceedings of the 23rd Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: Curran Associates, 2009. 673-681
[62]	Yang J, Yan R, Hauptmann A G. Cross-domain video concept detection using adaptive SVMs. In: Proceedings of the 15th international conference on Multimedia. Augsburg, Germany: ACM, 2007. 188-197
[63]	Jiang W, Zavesky E, Chang S F, Loui A. Cross-domain learning methods for high-level visual concept classification. In: Proceedings of the 15th IEEE International Conference. Adelaide, Australia: IEEE, 2008. 161-164
[64]	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 Press, 2009. 1327-1336
[65]	Duan L X, Tsang I W, Xu D. Domain transfer multiple kernel learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(3): 465-479
[66]	Jo Y, Oh A H. Aspect and sentiment unification model for online review analysis. In: Proceedings of the 4th International Conference on Web Search and Web Data Mining. Hong Kong, China: ACM, 2011. 815-824
[67]	Malandrakis N, Potamianos A, Iosif E. Kernel models for affective lexicon creation. In: Proceedings of the 12th Annual Conference of the International Speech Communication Association. Florence, Italy: ISCA press, 2011. 2977-2980
[68]	Li Q, Li H B, Ji H, Wang W, Zheng J, Huang F. Joint bilingual name tagging for parallel corpora. In: Proceedings of the 21st ACM International Conference on Information and Knowledge Management. Maui, USA: ACM, 2012. 1727-1731
[69]	Finkel J R, Manning C D. Hierarchical Bayesian domain adaptation. In: Proceedings of the 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Singapore: ACL, 2009. 602-610
[70]	Chelba C, Acero A. Adaptation of maximum entropy capitalizer: little data can help a lot. Computer Speech and Language, 2006, 20(4): 382-399
[71]	Arnold A, Nallapati R, Cohen W W. Exploiting feature hierarchy for transfer learning in named entity recognition. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics. Ohio, USA: ACL, 2008. 245-253
[72]	Jiang J, Zhai C X. Instance weighting for domain adaptation in NLP. In: Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics. Prague, Czech Republic: ACL, 2007. 22-23
[73]	Daum H, Marcu D. Domain adaptation for statistical classifiers. Artificial Intelligence Research, 2011, 26: 101-126
[74]	Glorot X, Bordes A, Bengio Y. Deep sparse rectifier neural networks. In: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, USA: JMLR, 2011. 315-323
[75]	Zeman D, Resnik P. Cross-language parser adaptation between related languages. In: Proceedings of the 3rd International Joint Conference on Natural Language Processing. Hyderabad, India: ACL, 2008. 35-42
[76]	Hara T, Miyao Y, Tsujii J. Evaluating the impact of re-training a lexical disambiguation model on domain adaptation of an HPSG parser. Trends in Parsing Technology. Netherlands: Springer, 2010, 43: 257-275
[77]	Xu R F, Xu J, Wang X L. Instance level transfer learning for cross lingual opinion analysis. In: Proceedings of the 2nd Workshop on Computational Approaches to Subjectivity and Sentiment Analysis. Portland, Oregon, USA: ACL, 2011. 182-188
[78]	Glorot X, Bordes A, Bengio Y. Domain adaptation for large-scale sentiment classification: a deep learning approach. In: Proceedings of the 28th International Conference on Machine Learning. Bellevue, Washington, USA: Omnipress, 2011. 513-520
[79]	Das D, Smith N A. Semi-supervised frame-semantic parsing for unknown predicates. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, Oregon, USA: ACL, 2011. 1435-1444
[80]	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, Czech Republic: ACL, 2007. 440-447
[81]	Bollegala D, Weir D J, Carroll J. Using multiple sources to construct a sentiment sensitive thesaurus for cross-domain sentiment classification. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Portland, Oregon, USA: ACL, 2011. 132-141
[82]	Pecina P, Toral A, Way A. Towards using web-crawled data for domain adaptation in statistical machine translation. In: Proceedings of the 15th Annual Conference of the European Association for Machine Translation. Leuven, Belgium: Springer, 2011. 63-72
[83]	Axelrod A, He X D, Gao J F. Domain adaptation via pseudo in-domain data selection. In: Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing. Portland, Oregon, USA: ACL, 2011. 355-362
[84]	Ding K, Jin L W. Incremental MQDF learning for writer adaptive handwriting recognition. In: Proceedings of the 2010 International Conference on Frontiers in Handwriting Recognition. Kolkata, India: IEEE, 2010. 559-564
[85]	Zhang X Y, Liu C L. Writer adaptation with style transfer mapping. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(7): 1773-1787
[86]	Faraji-Davar N, Decampos T, Windridge D, Kittler J, Christmas W. Domain adaptation in the context of sport video action recognition. In: Proceedings of the 25th Annual Conference on Neural Information Processing Systems. Sierra Nevada, Spain: Springer, 2011. 61-65
[87]	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. Montreal, Canada: ACM, 2009. 37-51
[88]	Duh K, Kirchhoff K. Learning to rank with partially-labeled data. In: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. Singapore: ACM, 2008. 251-258
[89]	Geng B, Yang L J, Xu C, Hua X S. Ranking model adaptation for domain-specific search. IEEE Transactions on Knowledge and Data Engineering, 2012, 24(4): 745-758
[90]	Tao Jian-Wen, Wang Shi-Tong. Kernel distribution consistency based local domain adaptation learning. Acta Automatica Sinica, 2013, 39(8): 1295-1309 (陶剑文, 王士同. 核分布一致局部领域适应学习. 自动化学报, 2013, 39(8): 1295-1309)
[91]	Gao Jun, Huang Li-Li, Sun Chang-Yin. A local weighted mean based domain adaptation learning framework. Acta Automatica Sinica, 2013, 39(7): 1037-1052 (皋军, 黄丽莉, 孙长银. 一种基于局部加权均值的领域适应学习框架. 自动化学报, 2013, 39(7): 1037-1052)
[92]	Tao Jian-Wen, Wang Shi-Tong. Kernel support vector machine for domain adaptation. Acta Automatica Sinica, 2012, 38(5): 797-811 (陶剑文, 王士同. 领域适应核支持向量机. 自动化学报, 2012, 38(5): 797-811)
[93]	Jenatton R, Audibert J Y, Bach F. Structured variable selection with sparsity-inducing norms. The Journal of Machine Learning Research, 2011, 12: 2777-2824
[94]	Poultney C, Chopra S, Cun Y L. Efficient learning of sparse representations with an energy-based model. In: Proceedings of the 20th Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: MIT Press, 2006. 1137-1144
[95]	Christoudias C, Urtasun R, Darrell T. Multi-view learning in the presence of view disagreement. The Computing Research Repository, 2012, 12(6): 1153-1159
[96]	Ganchev K, Graca J, Blitzer J, Taskar B. Multi-view learning over structured and non-identical outputs. The Computing Research Repository, 2012, 12(6): 1206-1214
[97]	Koehn P, Schroeder J. Experiments in domain adaptation for statistical machine translation. In: Proceedings of the 2nd Workshop on Statistical Machine Translation. Prague, Czech Republic: ACL, 2007. 224-227
[98]	He X. Using word dependent transition models in HMM based word alignment for statistical machine translation. In: Proceedings of the 2nd Workshop on Statistical Machine Translation. Prague, Czech Republic: ACL, 2007. 80-87
[99]	Koehn P, Hoang H, Birch A. Moses: open source toolkit for statistical machine translation. In: Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions. Prague, Czech Republic: ACL, 2007. 177-180
[100]	Matsoukas S, Rosti A V I, Zhang B. Discriminative corpus weight estimation for machine translation. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing. Singapore: ACL, 2009. 708-717
[101]	Och F J. Minimum error rate training in statistical machine translation. In: Proceedings of the 41st Annual Meeting on Association for Computational Linguistics. Sapporo Convention Center, Sapporo, Japan: ACL, 2003. 160-167
[102]	Lei D M, Ng A Y, Jordan M I. Latent dirichlet allocation. The Journal of Machine Learning Research, 2003, 3: 993-1022
[103]	Guo H L, Zhu H J, Guo Z L. Domain adaptation with latent semantic association for named entity recognition. In: Proceedings of the 2009 North American Chapter of the Association of Computational Linguistics. Boulder, Colorado, USA: ACL, 2009. 281-289

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域自适应学习研究进展

doi: 10.3724/SP.J.1004.2014.01576

作者简介:
孙正康中国石油大学（北京）地球物理与信息工程学院硕士研究生. 主要研究方向为机器学习，域自适应学习研究.E-mail：sunzhengkang@126.com

通讯作者:
刘建伟博士，中国石油大学（北京）副研究员. 主要研究方向为智能信息处理，机器学习，复杂系统分析，预测与控制，算法分析与设计.E-mail：liujw@cup.edu.cn

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Review and Research Development on Domain Adaptation Learning

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域自适应学习研究进展

doi: 10.3724/SP.J.1004.2014.01576

作者简介: 孙正康 中国石油大学（北京）地球物理与信息工程学院硕士研究生. 主要研究方向为机器学习，域自适应学习研究.E-mail：sunzhengkang@126.com

通讯作者: 刘建伟 博士，中国石油大学（北京）副研究员. 主要研究方向为智能信息处理，机器学习，复杂系统分析，预测与控制，算法分析与设计.E-mail：liujw@cup.edu.cn

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Review and Research Development on Domain Adaptation Learning

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作者简介:
孙正康中国石油大学（北京）地球物理与信息工程学院硕士研究生. 主要研究方向为机器学习，域自适应学习研究.E-mail：sunzhengkang@126.com

通讯作者:
刘建伟博士，中国石油大学（北京）副研究员. 主要研究方向为智能信息处理，机器学习，复杂系统分析，预测与控制，算法分析与设计.E-mail：liujw@cup.edu.cn