Конструктивные оценки полного скользящего контроля для пороговой классификации

Математическая биология и биоинформатика. 2011;6(2):173-189.

doi: 10.17537/2011.6.173.

1. Vapnik VN, Chervonenkis AIa. Teoriia raspoznavaniia obrazov (Image Recognition Theory). Moscow, 1974 (in Russ.).
2. Vorontsov KV. Optimization methods for linear and monotone correction in the algebraic approach to the recognition problem. Comp. Maths Math. Phys. 2000;40(1):159-168.
3. Guz IS. In: Matematicheskie metody raspoznavaniia obrazov-13 (Mathematical Methods in Image Reconstruction-13: book of abstracts of International Conference). Moscow, 2007; pp. 111-114 (in Russ.).
4. Rudakov KV, Vorontsov KV. Methods of optimization and monotone correction in the algebraic approach to the recognition problem. Doklady Mathematics. 1999;60(1):139.
5. Vorontsov KV. Kombinatornyi podkhod k otsenke kachestva obuchaemykh algoritmov. In: Matematicheskie voprosy kibernetiki (Mathematical Problems of Cybernetics). Moscow, 2004;13:5-36 (in Russ.).
6. Vorontsov KV. Combinatorial substantiation of learning algorithms. Comp. Maths Math. Phys. 2004;44(11): 1997-2009.
7. Vorontsov KV. Kombinatornye otsenki kachestva obucheniia po pretsedentam (Combinatorial Estimations of the Quality of Learning by Precedents). Doklady RAN. Doklady Akademii Nauk. 2004;394(2):175-178 (in Russ.).
8. Vorontsov KV. Combinatorial probability and the tightness of generalization bounds. Patters Recognition and Image Analysis. 2008;18(2):243-259. doi: 10.1134/S1054661808020090
9. Vorontsov KV. Splitting and similarity phenomena in the sets of classifiers and their effect on the probability of overfitting. Pattern Recognotion and Image Ananlysis. 2009;19(3):412-420. doi: 10.1134/S1054661809030055
10. Vorontsov KV. Tight bounds for the probability of overfitting. Doklady Mathematics. 2009;80(3):793-796. doi: 10.1134/S1064562409060032
11. Vapnik VN. Vosstanovlenie zavisimostei po empiricheskim dannym (Reconstruction of Dependences on Empirical Data). Moscow, 1979 (in Russ.).
12. Zhuravlev IuI. Problemy kibernetiki (Issues of Cybernetics). 1978;33:5-68 (in Russ.).
13. Rudakov KV. Algebraicheskaia teoriia universal'nykh i lokal'nykh ogranichenii dlia algoritmov raspoznavaniia [Dissertatsiia na soiskanie uchenoi stepeni d.f.-m.n.] (Algebraic Theory of Universal and Local Constraints for Recognition Algorythms [Ph.D. Thesis]). Moscow, 1992 (in Russ.).
14. Hastie T, Tibshirani R, Friedman J. The elements of statistical learning. Springer-Verlag, 2001. doi: 10.1007/978-0-387-21606-5
15. Vapnik V. The nature of statistical learning theory. 2nd edition. New York: Springer-Verlag, 2000. doi: 10.1007/978-1-4757-3264-1
16. Rudakov KV. In: Matematicheskie metody raspoznavaniia obrazov–VII: tez. dokl. (Mathematical Methods in Image Recognition-VII: book of abstracts) Moscow, 1995 (in Russ.).
17. Kohavi R. A study of cross-validation and bootstrap for accuracy estimation and model selection. In: 14th International Joint Conference on Artificial Intelligence Montreal, Quebec, Canada, 1995; 1137-1145. Available at: http://citeseer.ist.psu. edu/kohavi95study.html.
18. Mullin M, Sukthankar R. Complete cross-validation for nearest neighbor classifiers. In: Proceedings of International Conference on Machine Learning. 2000. Available at: http://citeseer.ist.psu.edu/309025.