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Why curriculum learning & selfpaced learning work in big/noisy data: A theoretical perspective
1.  Institute for Information and System Sciences and Ministry of, Education Key Lab of Intelligent Networks and Network Security, Xi'an Jiaotong University, Xi'an, Shaanxi, China, China, China, China 
References:
[1] 
S. Basu and J. Christensen, Teaching Classification Boundaries to Humans, Proceddings of the 27th AAAI Conference on Artificial Intelligence, 2013. 
[2] 
Y. Bengio, J. Louradour, R. Collobert and J. Westone, Curriculum Learning, Proceedings of the 26th International Conference on Machine Learning, (2009), 4148. doi: 10.1145/1553374.1553380. 
[3] 
C.C. Chang and C.J. Lin, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2 (2011), 127. Software available from: http://www.csie.ntu.edu.tw/~cjlin/libsvm. 
[4] 
X. Chen, A. Shrivastava and A. Gupta, NEIL: Extracting visual knowledge from web data, Proceedings of the IEEE International Conference on Computer Vision, (2013), 14091416. doi: 10.1109/ICCV.2013.178. 
[5] 
F. Cucker and S. Smale, On the mathematical foundations of learning, Bull. Amer. Math. Soc., 39 (2002), 149. doi: 10.1090/S0273097901009235. 
[6] 
F. Cucker and D. X. Zhou, Learning Theory: An Approximation Theory Viewpoint, Cambridge University Press, New York, NY, USA, 2007. doi: 10.1017/CBO9780511618796. 
[7] 
Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, Proceedings of the 13th International Conference on Machine Learning, 1996. 
[8] 
L. Jiang, D. Y. Meng, T. Mitamura and A. Hauptman, Easy samples first: Selfpaced reranking for multimedia search, Proceddings of the ACM International Conference on Multimedia, (2014), 547556. doi: 10.1145/2647868.2654918. 
[9] 
L. Jiang, D. Y. Meng, S. Yu, Z. Z. Lan, S. G. Shan and A. Hauptma, Selfpaced Learning with Diversity, Advances in Nerual Information Processing Systems 27, 2014. 
[10] 
L. Jiang and D. Y. Meng, Q. Zhao, S. G. Shan and A. Hauptman, Selfpaced Curriculum Learning, Proceddings of the 29th AAAI Conference on Artificial Intelligence, 2015. 
[11] 
F. Khan, X. Zhu and B. Mutlu, How do Humans Teach: On Curriculum Learning and Teaching Dimension, Advances in Nerual Information Processing Systems 24, 2011. 
[12] 
M. Kumar, B. Packer and D. Koller, Selfpaced Learning for Latent Variable Models, Advances in Nerual Information Processing Systems 23, 2010. 
[13] 
M. Kumar, H. Turki, D. Preston and D. Koller, Learning specficclass segmentation from diverse data, Proceedings of the IEEE International Conference on Computer Vision, 2011. 
[14] 
Y. Lee and K. Grauman, Learning the easy things first: Selfpaced visual category discovery, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2011), 17211728. doi: 10.1109/CVPR.2011.5995523. 
[15] 
T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves and J. Welling, NeverEnding Learning, Proceddings of the 29th AAAI Conference on Artificial Intelligence, 2015. 
[16] 
M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, The MIT Press, Cambridge, Massachusetts, London, England, 2012. 
[17] 
E. Ni and C Ling, Supervised learning with minimal effort, Advances in Knowledge Discovery and Data Mining, 6119 (2010), 476487. doi: 10.1007/9783642136726_45. 
[18] 
J. Supanvcivc and D. Ramana, Selfpaced learning for longterm tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013. 
[19] 
Y. Tang, Y. B. Yang and Y. Gao, Selfpaced Dictionary Learning for Image Classification, Proceddings of the ACM International Conference on Multimedia, (2012), 833836. doi: 10.1145/2393347.2396324. 
[20] 
K. Tang, V. Ramanathan, F. Li and D. Koller, Shifting weights: Adapting object detectors from image to video, Advances in Nerual Information Processing Systems 25, 2012. 
[21] 
V. Vapnik, Statistical Learning Theory, WileyInterscience, New York, 1998. 
[22] 
S. Yu, L. Jiang, Z. Mao, X. J. Chang, X. Z. Du, C. Gan, Z. Z. Lan, Z. W. Xu, X. C. Li, Y. Cai, A. Kumar, Y. Miao, L. Martin, N. Wolfe, S. C. Xu, H. Li, M. Lin, Z. G. Ma, Y. Yang, D. Y. Meng, S. G. Shan, P. D. Sahin, S. Burger, F. Metze, R. Singh, B. Raj, T. Mitamura, R. Stern and A. Hauptmann, CMUInformedia@ TRECVID 2014 Multimedia Event Detection (MED), TRECVID Video Retrieval Evaluation Workshop, 2014. 
[23] 
Q. Zhao, D. Y. Meng, L. Jiang, Q. Xie, Z. B. Xu and A. Hauptman, Selfpaced Matrix Factorization, Proceddings of the 29th AAAI Conference on Artificial Intelligence, 2015. 
show all references
References:
[1] 
S. Basu and J. Christensen, Teaching Classification Boundaries to Humans, Proceddings of the 27th AAAI Conference on Artificial Intelligence, 2013. 
[2] 
Y. Bengio, J. Louradour, R. Collobert and J. Westone, Curriculum Learning, Proceedings of the 26th International Conference on Machine Learning, (2009), 4148. doi: 10.1145/1553374.1553380. 
[3] 
C.C. Chang and C.J. Lin, LIBSVM: A library for support vector machines, ACM Transactions on Intelligent Systems and Technology, 2 (2011), 127. Software available from: http://www.csie.ntu.edu.tw/~cjlin/libsvm. 
[4] 
X. Chen, A. Shrivastava and A. Gupta, NEIL: Extracting visual knowledge from web data, Proceedings of the IEEE International Conference on Computer Vision, (2013), 14091416. doi: 10.1109/ICCV.2013.178. 
[5] 
F. Cucker and S. Smale, On the mathematical foundations of learning, Bull. Amer. Math. Soc., 39 (2002), 149. doi: 10.1090/S0273097901009235. 
[6] 
F. Cucker and D. X. Zhou, Learning Theory: An Approximation Theory Viewpoint, Cambridge University Press, New York, NY, USA, 2007. doi: 10.1017/CBO9780511618796. 
[7] 
Y. Freund and R. E. Schapire, Experiments with a new boosting algorithm, Proceedings of the 13th International Conference on Machine Learning, 1996. 
[8] 
L. Jiang, D. Y. Meng, T. Mitamura and A. Hauptman, Easy samples first: Selfpaced reranking for multimedia search, Proceddings of the ACM International Conference on Multimedia, (2014), 547556. doi: 10.1145/2647868.2654918. 
[9] 
L. Jiang, D. Y. Meng, S. Yu, Z. Z. Lan, S. G. Shan and A. Hauptma, Selfpaced Learning with Diversity, Advances in Nerual Information Processing Systems 27, 2014. 
[10] 
L. Jiang and D. Y. Meng, Q. Zhao, S. G. Shan and A. Hauptman, Selfpaced Curriculum Learning, Proceddings of the 29th AAAI Conference on Artificial Intelligence, 2015. 
[11] 
F. Khan, X. Zhu and B. Mutlu, How do Humans Teach: On Curriculum Learning and Teaching Dimension, Advances in Nerual Information Processing Systems 24, 2011. 
[12] 
M. Kumar, B. Packer and D. Koller, Selfpaced Learning for Latent Variable Models, Advances in Nerual Information Processing Systems 23, 2010. 
[13] 
M. Kumar, H. Turki, D. Preston and D. Koller, Learning specficclass segmentation from diverse data, Proceedings of the IEEE International Conference on Computer Vision, 2011. 
[14] 
Y. Lee and K. Grauman, Learning the easy things first: Selfpaced visual category discovery, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (2011), 17211728. doi: 10.1109/CVPR.2011.5995523. 
[15] 
T. Mitchell, W. Cohen, E. Hruschka, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves and J. Welling, NeverEnding Learning, Proceddings of the 29th AAAI Conference on Artificial Intelligence, 2015. 
[16] 
M. Mohri, A. Rostamizadeh and A. Talwalkar, Foundations of Machine Learning, The MIT Press, Cambridge, Massachusetts, London, England, 2012. 
[17] 
E. Ni and C Ling, Supervised learning with minimal effort, Advances in Knowledge Discovery and Data Mining, 6119 (2010), 476487. doi: 10.1007/9783642136726_45. 
[18] 
J. Supanvcivc and D. Ramana, Selfpaced learning for longterm tracking, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2013. 
[19] 
Y. Tang, Y. B. Yang and Y. Gao, Selfpaced Dictionary Learning for Image Classification, Proceddings of the ACM International Conference on Multimedia, (2012), 833836. doi: 10.1145/2393347.2396324. 
[20] 
K. Tang, V. Ramanathan, F. Li and D. Koller, Shifting weights: Adapting object detectors from image to video, Advances in Nerual Information Processing Systems 25, 2012. 
[21] 
V. Vapnik, Statistical Learning Theory, WileyInterscience, New York, 1998. 
[22] 
S. Yu, L. Jiang, Z. Mao, X. J. Chang, X. Z. Du, C. Gan, Z. Z. Lan, Z. W. Xu, X. C. Li, Y. Cai, A. Kumar, Y. Miao, L. Martin, N. Wolfe, S. C. Xu, H. Li, M. Lin, Z. G. Ma, Y. Yang, D. Y. Meng, S. G. Shan, P. D. Sahin, S. Burger, F. Metze, R. Singh, B. Raj, T. Mitamura, R. Stern and A. Hauptmann, CMUInformedia@ TRECVID 2014 Multimedia Event Detection (MED), TRECVID Video Retrieval Evaluation Workshop, 2014. 
[23] 
Q. Zhao, D. Y. Meng, L. Jiang, Q. Xie, Z. B. Xu and A. Hauptman, Selfpaced Matrix Factorization, Proceddings of the 29th AAAI Conference on Artificial Intelligence, 2015. 
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