Skip to main content
Log in

Lifelong machine learning: a paradigm for continuous learning

  • Perspective
  • Published:
Frontiers of Computer Science Aims and scope Submit manuscript

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

References

  1. Chen Z Y, Ma N Z, Liu B. Lifelong learning for sentiment classification. In: Proceedings of ACL Conference. 2015

    Google Scholar 

  2. Pan S J, Yang Q. A survey on transfer learning. IEEE Transaction on Knowledge and Data Engineering, 2010, 22(10): 1345–1359

    Article  Google Scholar 

  3. Caruana R. Multitask learning. Machine Learning, 1997, 28(1)

    Google Scholar 

  4. Thrun S, Mitchell T M. Lifelong robot learning. In: Steels L, ed. The Biology and Technology of Intelligent Autonomous Agents. Berlin: Springer, 1995, 165–196

    Chapter  Google Scholar 

  5. Thrun S. Is learning the n-th thing any easier than learning the first? Advances in Neural Information Processing Systems, 1996: 640–646

    Google Scholar 

  6. Silver D L, Mercer R E. The task rehearsal method of life-long learning: overcoming impoverished data. In: Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence. 2002, 90–101

    Google Scholar 

  7. Fei G L, Wang S, Liu B. Learning cumulatively to become more knowledgeable. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 1565–1574

    Chapter  Google Scholar 

  8. Ruvolo P, Eaton E. ELLA: an efficient lifelong learning algorithm. In: Proceedings of International Conference on Machine Learning. 2013, 507–515

    Google Scholar 

  9. Pentina A, Lampert C H. A PAC-Bayesian bound for lifelong learning. In: Proceedings of International Conference on Machine Learning. 2014, 991–999

    Google Scholar 

  10. Chen Z Y, Liu B. Topic modeling using topics from many domains, lifelong learning and big data. In: Proceedings of International Conference on Machine Learning. 2014

    Google Scholar 

  11. Liu Q, Liu B, Zhang Y L, Kim D S, Gao Z Q. Improving opinion aspect extraction using semantic similarity and aspect associations. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016

    Google Scholar 

  12. Shu L, Liu B, Xu H, Kim A. Separating entities and aspects in opinion targets using lifelong graph labeling. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, 2016

    Google Scholar 

  13. Mitchell T, Cohen W, Hruschka E, Talukdar P, Betteridge J, Carlson A, Dalvi B, Gardner M, Kisiel B, Krishnamurthy J, Lao N, Mazaitis K, Mohamed T, Nakashole N, Platanios E, Ritter A, Samadi M, Settles B, Wang R, Wijaya D, Gupta A, Chen X, Saparov A, Greaves M, Welling J. Never-ending learning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2302–2310

    Google Scholar 

  14. Tanaka F, Yamamura M. An approach to lifelong reinforcement learning through multiple environments. In: Proceedings of the 6th European Workshop on Learning Robots. 1997, 93–99

    Google Scholar 

  15. Bou Ammar H, Eaton E, Ruvolo P, Taylor M. Online multi-task learning for policy gradient methods. In: Proceedings of the 31st International Conference on Machine Learning. 2014, 1206–1214

    Google Scholar 

Download references

Acknowledgements

This work was supported in part by a grant from National Science Foundation (NSF) (IIS-1407927), a grant from NCI (R01CA192240), and a gift from Bosch.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bing Liu.

Additional information

Bing Liu is a professor of computer science at University of Illinois at Chicago, USA. His research interests include sentiment analysis and opinion mining, lifelong machine learning, data mining, machine learning, and natural language processing. He currently serves as the Chair of ACM SIGKDD. He is an ACM Fellow, AAAI Fellow, and IEEE Fellow.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, B. Lifelong machine learning: a paradigm for continuous learning. Front. Comput. Sci. 11, 359–361 (2017). https://doi.org/10.1007/s11704-016-6903-6

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11704-016-6903-6

Navigation