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Articles

Online Learning Using Multiple Times Weight Updating

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References

  • Bartlett, P., E. Hazan, and A. Rakhlin. 2007. Adaptive online gradient descent. Advances in Neural Information Processing Systems 20 (NIPS 2007), pp: 65-72.
  • Crammer, K., M. Dredze, and A. Kulesza. 2009. Multi-class confidence weighted algorithms. Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, pp: 496–504.
  • Dekel, O., R. Gilad-Bachrach, O. Shamir, and L. Xiao. 2010. Optimal distributed online prediction using mini-batches. Journal of Machine Learning Research 13:165–202 .
  • Dredze, M., K. Crammer, and A. Kulesza. 2009. Adaptive regularization of weight vectors. Machine Learning 91:1–33.
  • Dredze, M., and K. Crammer. 2008. Active learning with confidence. Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics, pp: 233-236.
  • Gentile, C., N. Cesa-Bianchi, and A. Conconi. 2005. A second-order perceptron algorithm. SIAM Journal on Computing 34 (3):640–68. doi:10.1137/S0097539703432542.
  • Gentile, C. 2001. A new approximate maximal margin classification algorithm. The Journal of Machine Learning Research 2:213–42.
  • Kale, S., E. Hazan, and A. Agarwal. 2007. Logarithmic regret algorithms for online convex optimization. Machine Learning 69 (2–3):169–92. doi:10.1007/s10994-007-5016-8.
  • Keshet, J., S. Shalev-Shwartz, Y. Singer, K. Crammer, and O. Dekel. 2006. Online passive-aggressive algorithms. The Journal of Machine Learning Research 7:551–85.
  • Lee, D. D., and K. Crammer. Learning via gaussian herding. Advances in Neural Information Processing Systems 23 (NIPS 2010), pp:451–59, 2010.
  • Lu, J., S. Hoi, J. Wang, and P. Zhao. 01 2013. Second order online collaborative filtering. Journal of Machine Learning Research. 29: 325–40.
  • Luo, H., A. Agarwal, Cesa-Bianchi, Nicolà and Langford, John. Efficient second order online learning via sketching. In Advances in Neural Information Processing System, pp: 902–10, 2016.
  • Orabona, F., and K. Crammer. New adaptive algorithms for online classification. Advances in Neural Information Processing Systems 23 (NIPS 2010), pp:1840–48, 2010.
  • Peilin Zhao, R. J., and S. C. H. Hoi. 2011. Double updating online learning. The Journal of Machine Learning Research 12:1587–615.
  • Pereira, F., M. Dredze, and K. Crammer. Confidence-weighted linear classification. Proceedings of the 25th international conference on Machine learning ACM, pp:264–71, 2008.
  • Rosenblatt, F. 1958. The perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review 65 (6):386. doi:10.1037/h0042519.
  • Shi, T., and J. Zhu. 2017. Online bayesian passive-aggressive learning. The Journal of Machine Learning Research 18 (33):1–39.
  • Steven, C. H., H. J. Wang, and P. Zhao. 2016. Soft confidence-weighted learning. ACM Transactions on Intelligent Systems and Technology (TIST) 8 (1):15.
  • wu, Y., S. Hoi, and T. Mei. 2014. Massive-scale online feature selection for sparse ultra-high dimensional data. ACM Transactions on Knowledge Discovery from Data 11:09.
  • Ye, J., L. Yang, and R. Jin. Online learning by ellipsoid method. Proceedings of 26th Annual International Conference on Machine Learning, pp:451–59, 2009.
  • Yi, L., and P. M. Long. 2002. The relaxed online maximum margin algorithm. Machine Learning 46 (1–3):361–87. doi:10.1023/A:1012435301888.
  • Yongdong Zhang Steven, C., H. Hoi Jialei Wang, and J. Wan. Solar: Scalable online learning algorithms for ranking. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, pp:1692–701, 2015.
  • Zha, P., S. C. Hoi, and J. Wang. 2014. libol: A library for online learning algorithms. The Journal of Machine Learning Research 15 (1):495–99.
  • Zhao, J. L. P., S. C. H. Hoi, and D. Sahoo. Online learning: A comprehensive survey. arXiv preprint, p arXiv:1802.02871, 2018.
  • Zinkevich M. Online convex programming and generalized infinitesimal gradient ascent. ICML’03 Proceedings of the Twentieth International Conference on International Conference on Machine Learning, p. 928–35, 2003.

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