Knowledge and Information Systems

, Volume 52, Issue 3, pp 687–707 | Cite as

Online transfer learning by leveraging multiple source domains

  • Qingyao Wu
  • Xiaoming Zhou
  • Yuguang YanEmail author
  • Hanrui WuEmail author
  • Huaqing Min
Regular Paper


Transfer learning aims to enhance performance in a target domain by exploiting useful information from auxiliary or source domains when the labeled data in the target domain are insufficient or difficult to acquire. In some real-world applications, the data of source domain are provided in advance, but the data of target domain may arrive in a stream fashion. This kind of problem is known as online transfer learning. In practice, there can be several source domains that are related to the target domain. The performance of online transfer learning is highly associated with selected source domains, and simply combining the source domains may lead to unsatisfactory performance. In this paper, we seek to promote classification performance in a target domain by leveraging labeled data from multiple source domains in online setting. To achieve this, we propose a new online transfer learning algorithm that merges and leverages the classifiers of the source and target domain with an ensemble method. The mistake bound of the proposed algorithm is analyzed, and the comprehensive experiments on three real-world data sets illustrate that our algorithm outperforms the compared baseline algorithms.


Transfer learning Online learning Online transfer learning multiple source domains 



Y. Yan and H. Wu are the corresponding authors. The authors would like to thank the reviewers for their useful and constructive suggestions. This research was supported by the Guangzhou Key Laboratory of Robotics and Intelligent Software under Grant No. 15180007, and National Natural Science Foundation of China (NSFC) Under Grant No. 61502177.


  1. 1.
    Amini M, Usunier N, Goutte C (2009) Learning from multiple partially observed views-an application to multilingual text categorization. In: Advances in neural information processing systems, pp 28–36Google Scholar
  2. 2.
    Chattopadhyay R, Sun Q, Fan W, Davidson I, Panchanathan S, Ye J (2012) Multisource domain adaptation and its application to early detection of fatigue. ACM Trans Knowl Discov Data 6(4):18CrossRefGoogle Scholar
  3. 3.
    Crammer K, Dekel O, Keshet J, Shalev-Shwartz S, Singer Y (2006) Online passive-aggressive algorithms. J Mach Learn Res 7:551–585MathSciNetzbMATHGoogle Scholar
  4. 4.
    Dredze M, Crammer K, Pereira F (2008) Confidence-weighted linear classification. In: Proceedings of the 25th international conference on machine learning. ACM, pp 264–271Google Scholar
  5. 5.
    Dredze M, Kulesza A, Crammer K (2010) Multi-domain learning by confidence-weighted parameter combination. Mach Learn 79(1–2):123–149MathSciNetCrossRefGoogle Scholar
  6. 6.
    Duan L, Tsang IW, Xu D, Chua T-S (2009) Domain adaptation from multiple sources via auxiliary classifiers. In: Proceedings of the 26th annual international conference on machine learning. ACM, pp 289–296Google Scholar
  7. 7.
    Eaton E, des Jardins M (2011) Selective transfer between learning tasks using task-based boosting. In: AAAIGoogle Scholar
  8. 8.
    Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Freund Y, Schapire RE (1999) Large margin classification using the perceptron algorithm. Mach Learn 37(3):277–296CrossRefzbMATHGoogle Scholar
  10. 10.
    Ge L, Gao J, Zhang A (2013) OMS-TL: A framework of online multiple source transfer learning. In: Proceedings of the 22nd ACM international conference on Information and knowledge management. ACM, pp 2423–2428Google Scholar
  11. 11.
    Han C, Tan Y-K, Zhu J-H, Guo Y, Chen J, Qing-Yao W (2016) Online feature selection of class imbalance via pa algorithm. J Comput Sci Technol 31(4):673–682MathSciNetCrossRefGoogle Scholar
  12. 12.
    Hoi SCH, Wang J, Zhao P (2014) Libol: a library for online learning algorithms. J Mach Learn Res 15(1):495–499zbMATHGoogle Scholar
  13. 13.
    Li G, Hoi SCH, Chang K, Liu W, Jain R (2014) Collaborative online multitask learning. IEEE Trans Knowl Data Eng 26(8):1866–1876CrossRefGoogle Scholar
  14. 14.
    Ng MK, Wu Q, Ye Y (2012) Co-transfer learning via joint transition probability graph based method. In: Proceedings of the 1st international workshop on cross domain knowledge discovery in web and social network mining, pp 1–9Google Scholar
  15. 15.
    Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359CrossRefGoogle Scholar
  16. 16.
    Rosenblatt F (1958) The perceptron: a probabilistic model for information storage and organization in the brain. Psychol Rev 65(6):386CrossRefGoogle Scholar
  17. 17.
    Schölkopf B, Herbrich R, Smola AJ (2001) A generalized representer theorem. In: Computational learning theory. Springer, pp 416–426Google Scholar
  18. 18.
    Shalev-Shwartz S, Singer Y (2007) Online learning: theory, algorithms, and applicationsGoogle Scholar
  19. 19.
    Wang J, Zhao P, Hoi SCH (2012) Exact soft confidence-weighted learning. arXiv preprint arXiv:1206.4612
  20. 20.
    Wang J, Hoi SCH, Zhao P, Liu Z-Y (2013) Online multi-task collaborative filtering for on-the-fly recommender systems. In: Proceedings of the 7th ACM conference on recommender systems. ACM, pp 237–244Google Scholar
  21. 21.
    Wu Q, Ng MK, Ye Y (2014) Cotransfer learning using coupled markov chains with restart. IEEE Intell Syst 29(4):26–33CrossRefGoogle Scholar
  22. 22.
    Xindong W, Chen H, Gongqing W, Liu J, Zheng Q, He X, Zhao Z-Q, Wei B, Li Y, Zhang Q et al (2015) Knowledge engineering with big data. IEEE Intell Syst 30(5):46–55CrossRefGoogle Scholar
  23. 23.
    Xiang EW, Pan SJ, Pan W, Su J, Yang Q (2011) Source-selection-free transfer learning. In: IJCAI proceedings-international joint conference on artificial intelligence, vol 22, p 2355Google Scholar
  24. 24.
    Yan Y, Wu Q, Tan M, Min H (2016) Online heterogeneous transfer learning by weighted offline and online classifiers. In: Proceedings of the 14th European conference on computer vision (ECCV) workshops, pp 467–474Google Scholar
  25. 25.
    Yao Y, Doretto G (2010) Boosting for transfer learning with multiple sources. In: Computer vision and pattern recognition (CVPR), 2010 IEEE conference on, pp 855–1862. IEEEGoogle Scholar
  26. 26.
    Zhao P, Hoi SC (2010) OTL: a framework of online transfer learning. In: Proceedings of the 27th international conference on machine learning (ICML-10), pp 1231–1238Google Scholar
  27. 27.
    Zhao P, Hoi SCH, Wang J, Li B (2014) Online transfer learning. Artif Intell 216:76–102MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  1. 1.School of Software EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations