A brief introduction to weakly supervised learning(简要介绍弱监督学习) (9)

Karger DR, Sewoong O and Devavrat S. Iterative learning for reliable crowd- sourcing systems. In Advances in Neural Information Processing Systems 24, Cambridge, MA: MIT Press, 2011; 1953–61. 

Tran-Thanh L, Venanzi M and Rogers A et al. Ef cient budget allocation with accuracy guarantees for crowdsourcing classi cation tasks. In 12th Interna- tional conference on Autonomous Agents and Multi-Agent Systems, Saint Paul, MN, 2013; 901–8. 

Ho CJ, Jabbari S and Vaughan JW. Adaptive task assignment for crowd- sourced classi cation. In 30th International Conference on Machine Learning, Atlanta, GA, 2013; 534–42. 

Chen X, Lin Q and Zhou D. Optimistic knowledge gradient policy for opti- mal budget allocation in crowdsourcing. In 30th International Conference on Machine Learning, Atlanta, GA, 2013; 64–72. 

Dawid AP and Skene AM. Maximum likelihood estimation of observer error- rates using the EM algorithm. J Roy Stat Soc C Appl Stat 1979; 28: 20– 8

Zhong J, Tang K and Zhou Z-H. Active learning from crowds with unsure op- tion. In 24th International Joint Conference on Arti cial Intelligence, Buenos Aires, Argentina, 2015; 1061–7. 

Ding YX and Zhou ZH. Crowdsourcing with unsure opinion. arXiv:1609.00292, 2016. 

Shah NB and Zhou D. Double or nothing: multiplicative incentive mechanisms for crowdsourcing. In Advances in Neural Information Processing Systems 28, Cambridge, MA: MIT Press, 2015; 1–9. 

Rahmani R and Goldman SA. MISSL: multiple-instance semi-supervised learn- ing. In 23rd International Conference on Machine Learning, Pittsburgh, PA, 2006; 705–12. 

Yan Y, Rosales R and Fung G et al. Active learning from crowds. In 28th Inter- national Conference on Machine Learning, Bellevue, WA, 2011; 1161–8. 

Sutton RS and Barto AG. Reinforcement Learning: An Introduction. Cambridge: MIT Press, 1998. 

Schwenker F and Trentin E. Partially supervised learning for pattern recognition. Pattern Recogn Lett 2014; 37: 1–3. 

Garcia-Garcia D and Williamson RC. Degrees of supervision. In Advances in Neural Information Processing Systems 17, Cambridge, MA: MIT Press Work- shops, 2011. 

Herna ́ ndez-Gonza ́ lez J, Inza I and Lozano JA. Weak supervision and other non-standard classification problems: a taxonomy. Pattern Recogn Lett 2016; 69: 49–55. 

KunchevaLI,Rod ́ıguezJJandJacksonAS.Restrictedsetclassi cation:who is there? Pattern Recogn 2017; 63:158–70. 

Zhang M-L and Zhou Z-H. A review on multi-label learning algorithms. IEEE Trans Knowl Data Eng 2014; 26: 1819–37.

Sun YY, Zhang Y and Zhou ZH. Multi-label learning with weak label. In 24th AAAI Conference on Arti cial Intelligence, Atlanta, GA, 2010; 593–8. 

Li X and Guo Y. Active learning with multi-label SVM classi cation. In 23rd International Joint Conference on Arti cial Intelligence, Beijing, China, 2013; 1479–85. 

Qi GJ, Hua XS and Rui Y et al. Two-dimensional active learning for image classi cation. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Anchorage, AK, 2008.

Huang SJ, Chen S and Zhou ZH. Multi-label active learning: query type matters. In 24th International Joint Conference on Arti cial Intelligence, Buenos Aires, Argentina, 2015; 946–52.

 

周志华:南京大学计算机软件新技术国家重点实验室(National Key Laboratory for Novel Software Technology)教授。NSR专题特邀编辑(Guest Editor of Special Topic of NSR)

科普一下:

《国家科学评论》(National Science Review, NSR)是我国第一份英文版自然科学综述性学术期刊,定位于全方位、多角度反映中外科学研究的重要成就,深度解读重大科技事件、重要科技政策,旨在展示世界(尤其是我国)前沿研究和热点研究的最新进展和代表性成果,引领学科发展,促进学术交流。NSR的报道范围涵盖数理科学、化学科学、生命科学、地球科学、材料科学、信息科学等六大领域。基于科睿唯安发布的2016年度的期刊引证报告(Journal Citation Reports,JCR),NSR的最新影响因子达到8.843,稳居全球多学科综合类期刊的第五名(8%,Q1)。NSR发表的所有论文全文可以在线免费阅读和下载。

内容版权声明:除非注明,否则皆为本站原创文章。

转载注明出处:https://www.heiqu.com/wpgwyz.html