Advertisement

Transfer Learning for Text Mining

  • Weike PanEmail author
  • Erheng Zhong
  • Qiang Yang
Chapter

Abstract

Over the years, transfer learning has received much attention in machine learning research and practice. Researchers have found that a major bottleneck associated with machine learning and text mining is the lack of high-quality annotated examples to help train a model. In response, transfer learning offers an attractive solution for this problem. Various transfer learning methods are designed to extract the useful knowledge from different but related auxiliary domains. In its connection to text mining, transfer learning has found novel and useful applications. In this chapter, we will review some most recent developments in transfer learning for text mining, explain related algorithms in detail, and project future developments of this field. We focus on two important topics: cross-domain text document classification and heterogeneous transfer learning that uses labeled text documents to help classify images.

Keywords

Transfer learning text mining classification clustering learning-torank 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Rie Kubota Ando and Tong Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. J. Mach. Learn. Res., 6:1817–1853, December 2005.Google Scholar
  2. 2.
    Andrew Arnold, Ramesh Nallapati, and William W. Cohen. A comparative study of methods for transductive transfer learning. In Proceedings of the Seventh IEEE International Conference on Data Mining Workshops, ICDMW ’07, pages 77–82, Washington, DC, USA, 2007. IEEE Computer Society.Google Scholar
  3. 3.
    Jing Bai, Ke Zhou, Guirong Xue, Hongyuan Zha, Gordon Sun, Belle Tseng, Zhaohui Zheng, and Yi Chang. Multi-task learning for learning to rank in web search. In Proceeding of the 18th ACM conference on Information and knowledge management, CIKM ’09, pages 1549–1552, New York, NY, USA, 2009. ACM.Google Scholar
  4. 4.
    Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, and Artur Dubrawski. Analysis of representations for domain adaptation. In NIPS, 2006.Google Scholar
  5. 5.
    Adam L. Berger, Vincent J. Della Pietra, and Stephen A. Della Pietra. A maximum entropy approach to natural language processing. Comput. Linguist., 22:39–71, March 1996.Google Scholar
  6. 6.
    Steffen Bickel, Michael Br¨uckner, and Tobias Scheffer. Discriminative learning for differing training and test distributions. In Proceedings of the 24th international conference on Machine learning, ICML ’07, pages 81–88, New York, NY, USA, 2007. ACM.Google Scholar
  7. 7.
    John Blitzer, Mark Dredze, and Fernando Pereira. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification. In Association for Computational Linguistics, Prague, Czech Republic.Google Scholar
  8. 8.
    John Blitzer, Ryan McDonald, and Fernando Pereira. Domain adaptation with structural correspondence learning. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, EMNLP ’06, pages 120–128, Stroudsburg, PA, USA, 2006. Association for Computational Linguistics.Google Scholar
  9. 9.
    Avrim Blum and Tom Mitchell. Combining labeled and unlabeled data with co-training. In Proceedings of the eleventh annual conference on Computational learning theory, COLT’ 98, pages 92–100, New York, NY, USA, 1998. ACM.Google Scholar
  10. 10.
    Karsten M. Borgwardt, Arthur Gretton, Malte J. Rasch, Hans- Peter Kriegel, Bernhard Sch¨olkopf, and Alexander J. Smola. Integrating structured biological data by kernel maximum mean discrepancy. In Proceedings of the 14th International Conference on Intelligent Systems for Molecular Biology, pages 49–57, Fortaleza, Brazil, August 2006.Google Scholar
  11. 11.
    Leo Breiman. Bagging predictors. Mach. Learn., 24:123–140, August 1996.Google Scholar
  12. 12.
    Lorenzo Bruzzone and Mattia Marconcini. Domain adaptation problems: A dasvm classification technique and a circular validation strategy. IEEE Trans. Pattern Anal. Mach. Intell., 32(5):770– 787, 2010.Google Scholar
  13. 13.
    Chris Burges, Tal Shaked, Erin Renshaw, Ari Lazier, Matt Deeds, Nicole Hamilton, and Greg Hullender. Learning to rank using gradient descent. In Proceedings of the 22nd international conference on Machine learning, ICML ’05, pages 89–96, New York, NY, USA, 2005. ACM.Google Scholar
  14. 14.
    Jian-Feng Cai, Emmanuel J. Cand`es, and Zuowei Shen. A singular value thresholding algorithm for matrix completion. SIAM J. on Optimization, 20:1956–1982, March 2010.Google Scholar
  15. 15.
    Peng Cai and Aoying Zhou. A novel framework for ranking model adaptation. Web Information Systems and Applications Conference, 0:149–154, 2010.Google Scholar
  16. 16.
    Bin Cao, Sinno Jialin Pan, Yu Zhang, Dit-Yan Yeung, and Qiang Yang. Adaptive transfer learning. In AAAI, 2010.Google Scholar
  17. 17.
    Rich Caruana. Multitask learning: A knowledge-based source of inductive bias. In ICML, pages 41–48, 1993.Google Scholar
  18. 18.
    Olivier Chapelle, Bernhard Sch¨olkopf, and Alexander Zien. Semi- Supervised Learning (Adaptive Computation and Machine Learning). The MIT Press, 2006.Google Scholar
  19. 19.
    Depin Chen, Yan Xiong, Jun Yan, Gui-Rong Xue, Gang Wang, and Zheng Chen. Knowledge transfer for cross domain learning to rank. Inf. Retr., 13:236–253, June 2010.Google Scholar
  20. 20.
    Wenyuan Dai, Yuqiang Chen, Gui-Rong Xue, Qiang Yang, and Yong Yu. Translated learning: Transfer learning across different feature spaces. In NIPS, pages 353–360, 2008.Google Scholar
  21. 21.
    Wenyuan Dai, Ou Jin, Gui-Rong Xue, Qiang Yang, and Yong Yu. Eigentransfer: a unified framework for transfer learning. In Proceedings of the 26th Annual International Conference on Machine Learning, ICML ’09, pages 193–200, New York, NY, USA, 2009. ACM.Google Scholar
  22. 22.
    Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu. Coclustering based classification for out-of-domain documents. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’07, pages 210– 219, New York, NY, USA, 2007. ACM.Google Scholar
  23. 23.
    Wenyuan Dai, Qiang Yang, Gui-Rong Xue, and Yong Yu. Boosting for transfer learning. In Proceedings of the 24th international conference on Machine learning, ICML ’07, pages 193–200, New York, NY, USA, 2007. ACM.Google Scholar
  24. 24.
    Wenyuan Dai, Qiang Yang, Gui-Rong Xue, and Yong Yu. Selftaught clustering. In Machine Learning, Proceedings of the Twenty-Fifth International Conference (ICML 2008), Helsinki, Finland, June 5-9, 2008, volume 307, pages 200–207. ACM, 2008.Google Scholar
  25. 25.
    Scott Deerwester, Susan T. Dumais, George W. Furnas, Thomas K. Landauer, and Richard Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41:391–407, 1990.CrossRefGoogle Scholar
  26. 26.
    Chuong B. Do and Andrew Y. Ng. Transfer learning for text classification. In NIPS, 2005.Google Scholar
  27. 27.
    Harris Drucker. Improving regressors using boosting techniques. In Proceedings of the Fourteenth International Conference on Machine Learning, ICML ’97, pages 107–115, San Francisco, CA, USA, 1997. Morgan Kaufmann Publishers Inc.Google Scholar
  28. 28.
    Eric Eaton and Marie desJardins. Set-based boosting for instancelevel transfer. In Proceedings of the 2009 IEEE International Conference on Data Mining Workshops, ICDMW ’09, pages 422–428, Washington, DC, USA, 2009. IEEE Computer Society.Google Scholar
  29. 29.
    Eric Eaton, Marie Desjardins, and Terran Lane. Modeling transfer relationships between learning tasks for improved inductive transfer. In Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases - Part I, ECML PKDD ’08, pages 317–332, Berlin, Heidelberg, 2008. Springer- Verlag.Google Scholar
  30. 30.
    Theodoros Evgeniou and Massimiliano Pontil. Regularized multi– task learning. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’04, pages 109–117, New York, NY, USA, 2004. ACM.Google Scholar
  31. 31.
    Yoav Freund and Robert E. Schapire. A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci., 55(1):119–139, 1997.Google Scholar
  32. 32.
    Jing Gao, Wei Fan, Jing Jiang, and Jiawei Han. Knowledge transfer via multiple model local structure mapping. In Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’08, pages 283–291, New YorkGoogle Scholar
  33. 33.
    NY, USA, 2008. ACM.Google Scholar
  34. 34.
    Wei Gao, Peng Cai, Kam-FaiWong, and Aoying Zhou. Learning to rank only using training data from related domain. In Proceeding of the 33rd international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’10, pages 162–169Google Scholar
  35. 35.
    New York, NY, USA, 2010. ACM.Google Scholar
  36. 36.
    Bo Geng, Linjun Yang, Chao Xu, and Xian-Sheng Hua. RankingGoogle Scholar
  37. 37.
    model adaptation for domain-specific search. In Proceeding of the 18th ACM conference on Information and knowledge management, CIKM ’09, pages 197–206, New York, NY, USA, 2009. ACM.Google Scholar
  38. 38.
    James J Heckman. Sample selection bias as a specification error. Econometrica, 47(1):153–61, January 1979.Google Scholar
  39. 39.
    David W. Hosmer and Stanley Lemeshow. Applied logistic regression. Wiley-Interscience, 2 edition, 2000.Google Scholar
  40. 40.
    Hal Daum´e III. Frustratingly easy domain adaptation. In ACL, 2007.Google Scholar
  41. 41.
    Jing Jiang. Multi-task transfer learning for weakly-supervised relation extraction. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume Google Scholar
  42. 42.
    2 - Volume 2, ACL ’09, pages 1012–1020, Stroudsburg, PA, USA, 2009. Association for Computational Linguistics.Google Scholar
  43. 43.
    Jing Jiang and ChengXiang Zhai. Instance weighting for domain adaptation in nlp. In ACL, 2007.Google Scholar
  44. 44.
    Wei Jiang, Eric Zavesky, Shih-Fu Chang, and Alexander C. Loui. Cross-domain learning methods for high-level visual concept classification. In ICIP, pages 161–164, 2008.Google Scholar
  45. 45.
    Thorsten Joachims. Transductive inference for text classification using support vector machines. In Proceedings of the Sixteenth International Conference on Machine Learning, ICML ’99, pages 200–209, San Francisco, CA, USA, 1999. Morgan Kaufmann Publishers Inc.Google Scholar
  46. 46.
    Thorsten Joachims. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Kluwer Academic Publishers, Norwell, MA, USA, 2002.Google Scholar
  47. 47.
    Thorsten Joachims. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’02, pages 133–142, New York, NY, USA, 2002. ACM.Google Scholar
  48. 48.
    Eyal Krupka and Naftali Tishby. Incorporating prior knowledge on features into learning. In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, SanGoogle Scholar
  49. 49.
    Juan, Puerto Rico, 2007.Google Scholar
  50. 50.
    S. Kullback and R. A. Leibler. On information and sufficiency. Annals of Mathematical Statistics, 22(1):79–86, 1951.MathSciNetzbMATHCrossRefGoogle Scholar
  51. 51.
    Abhishek Kumar, Avishek Saha, and Hal Daum´e III. A coregularization based semi-supervised domain adaptation. In Proceedings of the Conference on Neural Information Processing Systems (NIPS), Vancouver, Canada, 2010.Google Scholar
  52. 52.
    John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proceedings of the Eighteenth International Conference on Machine Learning, ICML ’01, pages 282– 289, San Francisco, CA, USA, 2001. Morgan Kaufmann Publishers Inc.Google Scholar
  53. 53.
    Ken Lang. Newsweeder: Learning to filter netnews. In Proceedings of the Twelfth International Conference on Machine Learning, 1995.Google Scholar
  54. 54.
    David Dolan Lewis. Reuters-21578 test collection. http://www.daviddlewis.com/.Google Scholar
  55. 55.
    Fei-Fei Li, Fergus Rob, and Perona Pietro. One-shot learning of object categories. IEEE Trans. Pattern Anal. Mach. Intell., 28:594–, April 2006.Google Scholar
  56. 56.
    Liangda Li, Ke Zhou, Gui-Rong Xue, Hongyuan Zha, and Yong Yu. Video summarization via transferrable structured learning. In Proceedings of the 20th international conference on World wide web, WWW ’11, pages 287–296, New York, NY, USA, 2011. ACM.Google Scholar
  57. 57.
    Xiao Li and Jeff Bilmes. Regularized adaptation of discriminative classifiers. In Proc. IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing, Toulouse, France, May 2006.Google Scholar
  58. 58.
    Xiao Li and Jeff Bilmes. A bayesian divergence prior for classifier adaptation. In Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-2007), March 2007.Google Scholar
  59. 59.
    Xiao Li, Jeff Bilmes, and Joh Malkin. Maximum margin learning and adaptation of MLP classifers. September 2005.Google Scholar
  60. 60.
    Xuejun Liao, Ya Xue, and Lawrence Carin. Logistic regression with an auxiliary data source. In Proceedings of the 22nd international conference on Machine learning, ICML ’05, pages 505–512, New York, NY, USA, 2005. ACM.Google Scholar
  61. 61.
    Xiao Ling, Gui-Rong Xue, Wenyuan Dai, Yun Jiang, Qiang Yang, and Yong Yu. Can chinese web pages be classified with english data source? In WWW, pages 969–978, 2008.Google Scholar
  62. 62.
    Mingsheng Long, Wei Cheng, Xiaoming Jin, Jianmin Wang, and Dou Shen. Transfer learning via cluster correspondence inference. In ICDM, pages 917–922, 2010.Google Scholar
  63. 63.
    Ulrike Luxburg. A tutorial on spectral clustering. Statistics and Computing, 17:395–416, December 2007.MathSciNetCrossRefGoogle Scholar
  64. 64.
    Olvi L. Mangasarian. Generalized support vector machines. Technical report, Computer Sciences Department, University of Wisconsin, 1998.Google Scholar
  65. 65.
    Yishay Mansour, Mehryar Mohri, and Afshin Rostamizadeh. Domain adaptation with multiple sources. In NIPS, 2008.Google Scholar
  66. 66.
    Andrew Kachites McCallum. Simulated/real/aviation/auto usenet data. http://www.cs.umass.edu/~mccallum/code-data.html.Google Scholar
  67. 67.
    Tiberio Caetano S. V. N. Vishwanathan Novi Quadrianto, Alex Smola and James Petterson. Multitask learning without label correspondences. In NIPS, 2010.Google Scholar
  68. 68.
    Sinno Jialin Pan, James T. Kwok, and Qiang Yang. Transfer learning via dimensionality reduction. In AAAI, pages 677–682, 2008.Google Scholar
  69. 69.
    Sinno Jialin Pan, Xiaochuan Ni, Jian-Tao Sun, Qiang Yang, and Zheng Chen. Cross-domain sentiment classification via spectral feature alignment. In WWW, pages 751–760, 2010.Google Scholar
  70. 70.
    Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, and Qiang Yang. Domain adaptation via transfer component analysis. In IJCAI, pages 1187–1192, 2009.Google Scholar
  71. 71.
    Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, and Qiang Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks, 22(2):199–210, 2011.Google Scholar
  72. 72.
    Sinno Jialin Pan and Qiang Yang. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10):1345–1359, October 2010.Google Scholar
  73. 73.
    Bo Pang and Lillian Lee. Opinion mining and sentiment analysis. Found. Trends Inf. Retr., 2:1–135, January 2008.Google Scholar
  74. 74.
    David Pardoe and Peter Stone. Boosting for regression transfer. In ICML, pages 863–870, 2010.Google Scholar
  75. 75.
    Peter Prettenhofer and Benno Stein. Cross-language text classification using structural correspondence learning. In ACL, pages 1118–1127, 2010.Google Scholar
  76. 76.
    Peter Prettenhofer and Benno Stein. Cross-lingual adaptation using structural correspondence learning. ACM TIST, 3(1), 2012.Google Scholar
  77. 77.
    Guo-Jun Qi, Charu Aggarwal, and Thomas Huang. Towards semantic knowledge propagation from text corpus to web images. In Proceedings of the 20th international conference on World wide web, WWW ’11, pages 297–306, New York, NY, USA, 2011. ACM.Google Scholar
  78. 78.
    Guo-Jun Qi, Charu Aggarwal, Yong Rui, Qi Tian, Shiyu Chang, and Thomas Huang. Towards cross-category knowledge propagation for learning visual concepts. In CVPR, 2011.Google Scholar
  79. 79.
    Novi Quadrianto, Alex J. Smola, Tiberio S. Caetano, S.V.N. Vishwanathan, and James Petterson. Multitask learning without label correspondences. In NIPS 23, 2010.Google Scholar
  80. 80.
    Rajat Raina, Alexis Battle, Honglak Lee, Benjamin Packer, and Andrew Y. Ng. Self-taught learning: transfer learning from unlabeled data. In Proceedings of the 24th international conference on Machine learning, ICML ’07, pages 759–766, New York, NY, USA, 2007. ACM.Google Scholar
  81. 81.
    Rajat Raina, Andrew Y. Ng, and Daphne Koller. Constructing informative priors using transfer learning. In Proceedings of the 23rd international conference on Machine learning, ICML ’06, pages 713–720, New York, NY, USA, 2006. ACM.Google Scholar
  82. 82.
    Adwait Ratnaparkhi. A maximum entropy model for part-ofspeech tagging. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, April 1996.Google Scholar
  83. 83.
    Marcus Rohrbach, Michael Stark, and Bernt Schiele. Evaluating knowledge transfer and zero-shot learning in a large-scale setting. In CVPR, 2011.Google Scholar
  84. 84.
    Marcus Rohrbach, Michael Stark, Gy¨orgy Szarvas, Iryna Gurevych, and Bernt Schiele. What helps where - and why? semantic relatedness for knowledge transfer. In CVPR, pages 910–917, 2010.Google Scholar
  85. 85.
    Stefan R¨uping. Incremental learning with support vector machines. In Proceedings of the 2001 IEEE International Conference on Data Mining, ICDM ’01, pages 641–642,Washington, DC, USA, 2001. IEEE Computer Society.Google Scholar
  86. 86.
    Sandeepkumar Satpal and Sunita Sarawagi. Domain adaptation of conditional probability models via feature subsetting. In Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases, PKDD 2007, pages 224–235, Berlin, Heidelberg, 2007. Springer-Verlag.Google Scholar
  87. 87.
    Bernhard Scholkopf and Alexander J. Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge, MA, USA, 2001.Google Scholar
  88. 88.
    Dou Shen, Rong Pan, Jian-Tao Sun, Jeffrey Junfeng Pan, Kangheng Wu, Jie Yin, and Qiang Yang. Query enrichment for web-query classification. ACM Trans. Inf. Syst., 24:320–352, July 2006.Google Scholar
  89. 89.
    Dou Shen, Jian-Tao Sun, Qiang Yang, and Zheng Chen. Building bridges for web query classification. In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’06, pages 131–138, New York, NY, USA, 2006. ACM.Google Scholar
  90. 90.
    Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22:888–905, August 2000.Google Scholar
  91. 91.
    Xiaoxiao Shi, Wei Fan, Qiang Yang, and Jiangtao Ren. Relaxed transfer of different classes via spectral partition. In ECML/PKDD, 2009.Google Scholar
  92. 92.
    Xiaoxiao Shi, Qi Liu, Wei Fan, Philip S. Yu, and Ruixin Zhu. Transfer learning on heterogenous feature spaces via spectral transformation. In ICDM, pages 1049–1054, 2010.Google Scholar
  93. 93.
    Hidetoshi Shimodaira. Improving predictive inference under covariate shift by weighting the log-likelihood function. Journal of Statistical Planning and Inference, 90(2):227–244, 2000.MathSciNetzbMATHCrossRefGoogle Scholar
  94. 94.
    Si Si, Dacheng Tao, and Bo Geng. Bregman divergence-based regularization for transfer subspace learning. IEEE Trans. Knowl. Data Eng., 22(7):929–942, 2010.Google Scholar
  95. 95.
    Ajit P. Singh and Geoffrey J. Gordon. Relational learning via collective matrix factorization. In KDD, pages 650–658, 2008.Google Scholar
  96. 96.
    Simon Tong and Daphne Koller. Support vector machine active learning with applications to text classification. J. Mach. Learn. Res., 2:45–66, March 2002.Google Scholar
  97. 97.
    Bo Wang, Jie Tang, Wei Fan, Songcan Chen, Zi Yang, and Yanzhu Liu. Heterogeneous cross domain ranking in latent space. In Proceeding of the 18th ACM conference on Information and knowledge management, CIKM ’09, pages 987–996, New York, NY, USA, 2009. ACM.Google Scholar
  98. 98.
    Hua-Yan Wang, Vincent Wenchen Zheng, Junhui Zhao, and Qiang Yang. Indoor localization in multi-floor environments with reduced effort. In PerCom, pages 244–252, 2010.Google Scholar
  99. 99.
    Yang Mu Lourenco Bandeira Ricardo Ricardo Youxi Wu Zhenyu Lu Tianyu Cao Xindong Wu Wei Ding, Tomasz F. Stepinski. Subkilometer crater discovery with boosting and transfer learning. ACM TIST, x(x), 201x.Google Scholar
  100. 100.
    Philip C. Woodland. Speaker adaptation for continuous density hmms: a review. In ISCA Tutorial and Research Workshop (ITRW) on Adaptation Methods for Speech Recognition, ITRW’ 01, pages 29–30, August 2001.Google Scholar
  101. 101.
    Pengcheng Wu and Thomas G. Dietterich. Improving svm accuracy by training on auxiliary data sources. In Proceedings of the twenty-first international conference on Machine learning, ICML ’04, pages 110–, New York, NY, USA, 2004. ACM.Google Scholar
  102. 102.
    Evan Wei Xiang, Bin Cao, Derek Hao Hu, and Qiang Yang. Bridging domains using world wide knowledge for transfer learning. IEEE Trans. Knowl. Data Eng., 22(6):770–783, 2010.Google Scholar
  103. 103.
    Evan Wei Xiang, Sinno Jialin Pan, Weik Pan, Qiang Yang, and Jian Su. Source-free transfer learning. In IJCAI, 2011.Google Scholar
  104. 104.
    Jun Xu and Hang Li. Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR ’07, pages 391–398, New York, NY, USA, 2007. ACM.Google Scholar
  105. 105.
    Jun Yang, Rong Yan, and Alexander G. Hauptmann. Crossdomain video concept detection using adaptive svms. In Proceedings of the 15th international conference on Multimedia, MULTIMEDIA ’07, pages 188–197, New York, NY, USA, 2007. ACM.Google Scholar
  106. 106.
    Qiang Yang, Yuqiang Chen, Gui-Rong Xue, Wenyuan Dai, and Yong Yu. Heterogeneous transfer learning for image clustering via the social web. In Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1, ACL ’09, pages 1–9, Stroudsburg, PA, USA, 2009. Association for Computational Linguistics.Google Scholar
  107. 107.
    Yi Yao and Gianfranco Doretto. Boosting for transfer learning with multiple sources. In CVPR, pages 1855–1862, 2010.Google Scholar
  108. 108.
    Bianca Zadrozny. Learning and evaluating classifiers under sample selection bias. In Proceedings of the twenty-first international conference on Machine learning, ICML ’04, pages 114–, New York, NY, USA, 2004. ACM.Google Scholar
  109. 109.
    Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. Kernel methods for relation extraction. J. Mach. Learn. Res., 3:1083– 1106, March 2003.Google Scholar
  110. 110.
    Duo Zhang, Qiaozhu Mei, and ChengXiang Zhai. Cross-lingual latent topic extraction. In ACL, pages 1128–1137, 2010.Google Scholar
  111. 111.
    Tong Zhang and David Johnson. A robust risk minimization based named entity recognition system. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4, CONLL ’03, pages 204–207, Stroudsburg, PA, USA, 2003. Association for Computational Linguistics.Google Scholar
  112. 112.
    Yi Zhang, Jeff Schneider, and Artur Dubrawski. Learning the semantic correlation: An alternative way to gain from unlabeled text. In NIPS, pages 1945–1952, 2008.Google Scholar
  113. 113.
    Vincent Wenchen Zheng, Derek Hao Hu, and Qiang Yang. Crossdomain activity recognition. In Proceedings of the 11th international conference on Ubiquitous computing, Ubicomp ’09, pages 61–70, New York, NY, USA, 2009. ACM.Google Scholar
  114. 114.
    Erheng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiangtao Ren, Deepak Turaga, and Olivier Verscheure. Cross domain distribution adaptation via kernel mapping. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD ’09, pages 1027–1036, New York, NY, USA, 2009. ACM.Google Scholar
  115. 115.
    Erheng Zhong, Wei Fan, Qiang Yang, Olivier Verscheure, and Jiangtao Ren. Cross validation framework to choose amongst models and datasets for transfer learning. In ECML/PKDD (3), pages 547–562, 2010.Google Scholar
  116. 116.
    Xiaojin Zhu, Andrew B. Goldberg, Ronald Brachman, and Thomas Dietterich. Introduction to Semi-Supervised Learning. Morgan and Claypool Publishers, 2009.Google Scholar
  117. 117.
    Yin Zhu, Yuqiang Chen, Zhongqi Lu, Sinno Jialin Pan, Gui-Rong Xue, Yong Yu, and Qiang Yang. Heterogeneous transfer learning for image classification. In AAAI, 2011.Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Hong Kong University of Science and TechnologyKowloonHong Kong

Personalised recommendations