1.
Agarwal, S.: A study of the bipartite ranking problem in machine learning (2005)
2.
Cai, D., He, X., Han, J.: Locally consistent concept factorization for document clustering. IEEE Trans. Knowl. Data Eng.
23(6), 902–913 (2011)
CrossRef
3.
Cavallanti, G., Cesa-Bianchi, N., Gentile, C.: Tracking the best hyperplane with a simple budget perceptron. Mach. Learn.
69(2–3), 143–167 (2007)
CrossRef
4.
Cesa-Bianchi, N., Conconi, A., Gentile, C.: A second-order perceptron algorithm. SIAM J. Comput.
34(3), 640–668 (2005)
MathSciNetCrossRefMATH
5.
Chapelle, O., Keerthi, S.S.: Efficient algorithms for ranking with svms. Inf. Retrieval
13(3), 201–215 (2010)
CrossRef
6.
Cortes, C., Mohri, M.: Auc optimization vs. error rate minimization. In: Advances in Neural Information Processing Systems 16(16), pp. 313–320 (2004)
7.
Crammer, K., Dekel, O., Keshet, J., Shalev-Shwartz, S., Singer, Y.: Online passive-aggressive algorithms. J. Mach. Learn. Res.
7, 551–585 (2006)
MathSciNetMATH
8.
Crammer, K., Dredze, M., Kulesza, A.: Multi-class confidence weighted algorithms. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 2, pp. 496–504. Association for Computational Linguistics (2009)
9.
Crammer, K., Dredze, M., Pereira, F.: Confidence-weighted linear classification for text categorization. J. Mach. Learn. Res.
13(1), 1891–1926 (2012)
MathSciNetMATH
10.
Crammer, K., Kulesza, A., Dredze, M.: Adaptive regularization of weight vectors. In: Advances in Neural Information Processing Systems, pp. 414–422 (2009)
11.
Dekel, O., Shalev-Shwartz, S., Singer, Y.: The forgetron: a kernel-based perceptron on a budget. SIAM J. Comput.
37(5), 1342–1372 (2008)
MathSciNetCrossRefMATH
12.
Ding, Y., Zhao, P., Hoi, S.C., Ong, Y.S.: An adaptive gradient method for online auc maximization. In: AAAI, pp. 2568–2574 (2015)
13.
Dredze, M., Crammer, K., Pereira, F.: Confidence-weighted linear classification. In: Proceedings of the 25th International Conference on Machine Learning, pp. 264–271. ACM (2008)
14.
Duchi, J., Hazan, E., Singer, Y.: Adaptive subgradient methods for online learning and stochastic optimization. J. Mach. Learn. Res.
12, 2121–2159 (2011)
MathSciNetMATH
15.
Gao, W., Jin, R., Zhu, S., Zhou, Z.H.: One-pass auc optimization. In: ICML (3), pp. 906–914 (2013)
16.
Hanley, J.A., McNeil, B.J.: The meaning and use of the area under a receiver operating characteristic (roc) curve. Radiology
143(1), 29–36 (1982)
CrossRef
17.
Hu, J., Yang, H., King, I., Lyu, M.R., So, A.M.C.: Kernelized online imbalanced learning with fixed budgets. In: AAAI, pp. 2666–2672 (2015)
18.
Joachims, T.: A support vector method for multivariate performance measures. In: Proceedings of the 22nd International Conference on Machine Learning, pp. 377–384. ACM (2005)
19.
Joachims, T.: Training linear svms in linear time. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–226. ACM (2006)
20.
Kar, P., Sriperumbudur, B.K., Jain, P., Karnick, H.: On the generalization ability of online learning algorithms for pairwise loss functions. In: ICML (3), pp. 441–449 (2013)
21.
Kotlowski, W., Dembczynski, K.J., Huellermeier, E.: Bipartite ranking through minimization of univariate loss. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 1113–1120 (2011)
22.
Kuo, T.M., Lee, C.P., Lin, C.J.: Large-scale kernel ranksvm. In: SDM, pp. 812–820. SIAM (2014)
23.
Lee, C.P., Lin, C.B.: Large-scale linear ranksvm. Neural Comput.
26(4), 781–817 (2014)
MathSciNetCrossRef
24.
Li, N., Jin, R., Zhou, Z.H.: Top rank optimization in linear time. In: Advances in Neural Information Processing Systems, pp. 1502–1510 (2014)
25.
Liu, T.Y.: Learning to rank for information retrieval. Found. Trends Inf. Retrieval
3(3), 225–331 (2009)
CrossRef
26.
Ma, J., Kulesza, A., Dredze, M., Crammer, K., Saul, L.K., Pereira, F.: Exploiting feature covariance in high-dimensional online learning. In: International Conference on Artificial Intelligence and Statistics, pp. 493–500 (2010)
27.
Orabona, F., Crammer, K.: New adaptive algorithms for online classification. In: Advances in Neural Information Processing Systems, pp. 1840–1848 (2010)
28.
Orabona, F., Keshet, J., Caputo, B.: Bounded kernel-based online learning. J. Mach. Learn. Res.
10, 2643–2666 (2009)
MathSciNetMATH
29.
Rendle, S., Balby Marinho, L., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 727–736. ACM (2009)
30.
Rosenblatt, F.: The perceptron: a probabilistic model for information storage and organization in the brain. Psychol. Rev.
65(6), 386 (1958)
CrossRef
31.
Sculley, D.: Large scale learning to rank. In: NIPS Workshop on Advances in Ranking, pp. 1–6 (2009)
32.
Vitter, J.S.: Random sampling with a reservoir. ACM Trans. Math. Softw. (TOMS)
11(1), 37–57 (1985)
MathSciNetCrossRefMATH
33.
Wan, J., Wu, P., Hoi, S.C., Zhao, P., Gao, X., Wang, D., Zhang, Y., Li, J.: Online learning to rank for content-based image retrieval. In: Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, IJCAI, pp. 2284–2290 (2015)
34.
Wang, J., Wan, J., Zhang, Y., Hoi, S.C.: Solar: Scalable online learning algorithms for ranking
35.
Wang, J., Zhao, P., Hoi, S.C.: Exact soft confidence-weighted learning. In: Proceedings of the 29th International Conference on Machine Learning (ICML-12), pp. 121–128 (2012)
36.
Wang, J., Zhao, P., Hoi, S.C., Jin, R.: Online feature selection and its applications. IEEE Trans. Knowl. Data Eng.
26(3), 698–710 (2014)
CrossRef
37.
Zhao, P., Jin, R., Yang, T., Hoi, S.C.: Online auc maximization. In: Proceedings of the 28th International Conference on Machine Learning (ICML 2011), pp. 233–240 (2011)
38.
Zinkevich, M.: Online convex programming and generalized infinitesimal gradient ascent (2003)