Free Access
Volume 24, 2020
Page(s) 69 - 99
Published online 27 February 2020
  1. A. Agarwal, Selective sampling algorithms for cost-sensitive multiclass prediction, in Proceedings of the 30th International Conference on Machine Learning, edited by S. Dasgupta and D. McAllester, Vol. 28 of Proceedings of Machine Learning Research. Atlanta, Georgia, USA, (2013) 1220–1228. [Google Scholar]
  2. H. Ahn and K.-J. Kim, Corporate credit rating using multiclass classification models with order information. World Acad. Sci. Eng. Technol. 60 (2011) 95–100. [Google Scholar]
  3. E.L. Allwein, R.E. Schapire and Y. Singer, Reducing multiclass to binary: a unifying approach for margin classifiers. J. Mach. Learn. Res. 1 (2001) 113–141. [Google Scholar]
  4. J.-Y. Audibert and A.B. Tsybakov, Fast learning rates for plug-in classifiers. Ann. Stat. 35 (2007) 608–633. [Google Scholar]
  5. D. Belomestny and V. Spokoiny, Spatial aggregation of local likelihood estimates with applications to classification. Ann. Stat. 35 (2007) 2287–2311. [Google Scholar]
  6. C. Butucea, J.-F. Delmas, A. Dutfoy and R. Fischer, Optimal exponential bounds for aggregation of estimators for the Kullback-Leibler loss. Electron. J. Stat. 11 (2017) 2258–2294. [Google Scholar]
  7. T.I. Cannings, T.B. Berrett and R.J. Samworth, Local nearest neighbour classification with applications to semi-supervisedlearning (2017), [Google Scholar]
  8. K. Chaudhuri and S. Dasgupta, Rates of convergence for nearest neighbor classification, in Vol. 2 of Proceedings of the 27th International Conference on Neural Information Processing Systems, NIPS’14, Cambridge, MA, USA (2014) 3437–3445. [Google Scholar]
  9. K. Crammer and Y. Singer, On the algorithmic implementation of multiclass kernel-based vector machines. J. Mach. Learn. Res. 2 (2002) 265–292. [Google Scholar]
  10. D. Dai, P. Rigollet and T. Zhang, Deviation optimal learning using greedy Q-aggregation. Ann. Stat. 40 (2012) 1878–1905. [Google Scholar]
  11. A. Daniely, S. Sabato and . S.S. Shwartz, Multiclass learning approaches: A theoretical comparison with implications, in Advancesin Neural Information Processing Systems 25, edited by F. Pereira, C.J.C. Burges, L. Bottou, and K.Q. Weinberger, Curran Associates Inc. (2012), 485–493. [Google Scholar]
  12. D. Dheeru and E. Karra Taniskidou UCI machine learning repository, 2017. [Google Scholar]
  13. T.G. Dietterich and G. Bakiri, Solving multiclass learning problems via error-correcting output codes. J. Artif. Int. Res. 2 (1995) 263–286. [Google Scholar]
  14. C.H.Q. Ding and I. Dubchak, Multi-class protein fold recognition using support vector machines and neural networks. Bioinformatics 17 (2001) 349–358. [CrossRef] [PubMed] [Google Scholar]
  15. V. Dinh, L.S.T. Ho, N.V. Cuong, D. Nguyen and B.T. Nguyen, in Theory and Applications of Models of Computation, Vol. 9076 of Lecture Notes in Computer Sciences. Springer, Cham (2015) 375–387. [CrossRef] [Google Scholar]
  16. M. Döring, L. Györfi and H. Walk, Rate of convergence of k-nearest-neighbor classification rule. J. Mach. Learn. Res., 18 (2017) 16. [Google Scholar]
  17. S. Gadat, T. Klein and C. Marteau, Classification in general finite dimensional spaces with the k-nearest neighbor rule. Ann. Stat. 44 (2016) 982–1009. [Google Scholar]
  18. A. Ganapathiraju, J.E. Hamaker and J. Picone, Application of support vector machines to speech recognition. IEEE Trans. Signal Process. 52 (2004) 2348–2355. [Google Scholar]
  19. A. Juditsky, P. Rigollet and A.B. Tsybakov, Learning by mirror averaging. Ann. Stat. 36 (2008) 2183–2206. [Google Scholar]
  20. J. Kittler, R. Ghaderi, T. Windeatt and J. Matas. Face verification via error correcting output codes. Image Vis. Comput. 21 (2003) 1163–1169. [Google Scholar]
  21. G. Lecué, Optimal rates of aggregation in classification under low noise assumption. Bernoulli 13 (2007) 1000–1022. [CrossRef] [Google Scholar]
  22. G. Lecué, Empirical risk minimization is optimal for the convex aggregation problem. Bernoulli 19 (2013) 2153–2166. [CrossRef] [Google Scholar]
  23. G. Lecué and P. Rigollet, Optimal learning with Q-aggregation. Ann. Stat. 42 (2014) 211–224. [Google Scholar]
  24. E. Mammen and A.B. Tsybakov, Smooth discrimination analysis. Ann. Stat. 27 (1999) 1808–1829. [Google Scholar]
  25. V. Perchet and P. Rigollet. The multi-armed bandit problem with covariates. Ann. Stat. 41 (2013) 693–721. [Google Scholar]
  26. R. Rifkin and A. Klautau, In defense of one-vs-all classification. J. Mach. Learn. Res. 5 (2003/04) 101–141. [Google Scholar]
  27. P. Rigollet, Kullback-Leibler aggregation and misspecified generalized linear models. Ann. Stat. 40 (2012) 639–665. [Google Scholar]
  28. P. Rigollet and A.B. Tsybakov, Sparse estimation by exponential weighting. Stat. Sci. 27 (2012) 558–575. [Google Scholar]
  29. B.I. Rubinstein, P.L. Bartlett and J.H. Rubinstein, Shifting, one-inclusion mistake bounds and tight multiclass expected risk bounds, in Advances in Neural Information Processing Systems 19, edited by B. Schölkopf, J.C. Platt, and T. Hoffman, MIT Press, Cambridge (2007) 1193–1200. [Google Scholar]
  30. R.J. Samworth, Optimal weighted nearest neighbour classifiers. Ann. Stat. 40 (2012) 2733–2763. [Google Scholar]
  31. V. Spokoiny and C. Vial, Parameter tuning in pointwise adaptation using a propagation approach. Ann. Stat. 37 (2009) 2783–2807. [Google Scholar]
  32. R. Tibshirani, T. Hastie, B. Narasimhan and G. Chu, Class prediction by nearest shrunken centroids, with applications to dna microarrays. Stat. Sci. 18 (2003) 02. [Google Scholar]
  33. A.B. Tsybakov, Optimal Rates of Aggregation, Springer, Berlin (2003) 303–313. [Google Scholar]
  34. A.B. Yuditskiĭ, A.V. Nazin, A.B. Tsybakov and N. Vayatis, Recursive aggregation of estimators by the mirror descent method with averaging. Problemy Peredachi Informatsii 41 (2005) 78–96. [Google Scholar]

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