Lifelong machine learning: a paradigm for continuous learning

Bing LIU

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PDF(195 KB)
Front. Comput. Sci. ›› 2017, Vol. 11 ›› Issue (3) : 359-361. DOI: 10.1007/s11704-016-6903-6
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Lifelong machine learning: a paradigm for continuous learning

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Bing LIU. Lifelong machine learning: a paradigm for continuous learning. Front. Comput. Sci., 2017, 11(3): 359‒361 https://doi.org/10.1007/s11704-016-6903-6

References

[1]
ChenZ Y, MaN Z, LiuB. Lifelong learning for sentiment classification. In: Proceedings of ACL Conference. 2015
CrossRef Google scholar
[2]
PanS J, YangQ. A survey on transfer learning. IEEE Transaction on Knowledge and Data Engineering, 2010, 22(10): 1345–1359
CrossRef Google scholar
[3]
CaruanaR. Multitask learning. Machine Learning, 1997, 28(1)
CrossRef Google scholar
[4]
ThrunS, Mitchell T M. Lifelong robot learning. In: Steels L, ed. The Biology and Technology of Intelligent Autonomous Agents. Berlin: Springer, 1995, 165–196
CrossRef Google scholar
[5]
ThrunS. Is learning the n-th thing any easier than learning the first? Advances in Neural Information Processing Systems, 1996: 640–646
[6]
SilverD L, MercerR 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
CrossRef Google scholar
[7]
FeiG L, WangS, LiuB. Learning cumulatively to become more knowledgeable. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2016, 1565–1574
CrossRef Google scholar
[8]
RuvoloP, EatonE. ELLA: an efficient lifelong learning algorithm. In: Proceedings of International Conference on Machine Learning. 2013, 507–515
[9]
PentinaA, Lampert C H. A PAC-Bayesian bound for lifelong learning. In: Proceedings of International Conference on Machine Learning. 2014, 991–999
[10]
ChenZ Y, LiuB. Topic modeling using topics from many domains, lifelong learning and big data. In: Proceedings of International Conference on Machine Learning. 2014
[11]
LiuQ, LiuB, ZhangY L, Kim D S, GaoZ Q . Improving opinion aspect extraction using semantic similarity and aspect associations. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence. 2016
[12]
ShuL, LiuB, XuH, KimA. Separating entities and aspects in opinion targets using lifelong graph labeling. In: Proceedings of Conference on Empirical Methods in Natural Language Processing, 2016
[13]
MitchellT, CohenW, HruschkaE, Talukdar P, BetteridgeJ , CarlsonA, DalviB, GardnerM, Kisiel B, KrishnamurthyJ , LaoN, Mazaitis K, MohamedT , NakasholeN, Platanios E, RitterA , SamadiM, Settles B, WangR , WijayaD, GuptaA, ChenX, Saparov A, GreavesM , WellingJ. Never-ending learning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence. 2015, 2302–2310
[14]
TanakaF, Yamamura M. An approach to lifelong reinforcement learning through multiple environments. In: Proceedings of the 6th European Workshop on Learning Robots. 1997, 93–99
[15]
Bou AmmarH, EatonE, RuvoloP, Taylor M. Online multi-task learning for policy gradient methods. In: Proceedings of the 31st International Conference on Machine Learning. 2014, 1206–1214

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