|
|
|
|
References
|
|
| |
| 1. |
Freund Y., Schapire R. E., “A decision-theoretic generalization of on-line learning and an application to boosting”, J. of Comput. and Syst. Sci., 55:1 (1997), 119–139 |
| 2. |
Kanamori T., Takenouchi T., Eguchi S., Murata N., “Robust loss functions for boosting”, Neural Computation, 19:8 (2007), 2183–2244 |
| 3. |
Holland P. W., Welsch R. E., “Robust regression using iteratively reweighted least squares”, Communications in Statistics — Theory and Methods, 6:9 (1977), 813–827 |
| 4. |
Rousseeuw P. J., Leroy A. M., Robust Regression and Outlier Detection, John Wiley and Sons, New York, 1987 |
| 5. |
Rousseeuw P. J., Hubert M., “High-breakdown estimators of multivariate location and scatter”, Robustness and Complex Data Structures, eds. Becker C., Fried R., Kuhnt S., Springer, 2013, 49–66 |
| 6. |
Shibzukhov Z. M., “O printsipe minimizatsii empiricheskogo riska na osnove usrednyayuschikh agregiruyuschikh funktsii”, Dokl. RAN, 476:5 (2017), 495–499 |
| 7. |
Shibzukhov Z. M., “Machine learning based on the principle of minimizing robust mean estimates”, Advances in Intelligent Systems and Computing, 1310, Springer International Publishing, 2020, 472–477 |
| 8. |
Friedman J. H., “Greedy function approximation: A gradient boosting machine”, Annals Statist., 29:5 (2001) |
| 9. |
Csiszar I., Tusnady G., “Information geometry and alternating minimization procedures”, Statistics and Decisions, 1984, no. 1, Supplement Issue, 205–237 |
| 10. |
Calvo T., Beliakov G., “Aggregation functions based on penalties”, Fuzzy Sets and Systems, 161:10 (2010), 1420–1436 |
| 11. |
Shibzukhov Z. M., Semenov T. A., “Machine learning based on minimizing robust mean estimates”, Pattern Recognition, ICPR International Workshops and Challenges, Springer International Publishing, 2021, 112–119 |
| 12. |
Kingma D. P., Ba J., Adam: A method for stochastic optimization |
| 13. |
Huber P. J., Robust Statistics, John Wiley and Sons, 1981 |