IDEAS home Printed from https://ideas.repec.org/r/bes/jnlasa/v101y2006p138-156.html
   My bibliography  Save this item

Convexity, Classification, and Risk Bounds

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020. "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers 15418, C.E.P.R. Discussion Papers.
  2. Steffen Borgwardt & Rafael M. Frongillo, 2019. "Power Diagram Detection with Applications to Information Elicitation," Journal of Optimization Theory and Applications, Springer, vol. 181(1), pages 184-196, April.
  3. Wang, Shenhao & Wang, Qingyi & Bailey, Nate & Zhao, Jinhua, 2021. "Deep neural networks for choice analysis: A statistical learning theory perspective," Transportation Research Part B: Methodological, Elsevier, vol. 148(C), pages 60-81.
  4. Yang, Yi & Guo, Yuxuan & Chang, Xiangyu, 2021. "Angle-based cost-sensitive multicategory classification," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
  5. Seyed Mahdi Miraftabzadeh & Michela Longo & Federica Foiadelli & Marco Pasetti & Raul Igual, 2021. "Advances in the Application of Machine Learning Techniques for Power System Analytics: A Survey," Energies, MDPI, vol. 14(16), pages 1-24, August.
  6. Aakil M. Caunhye & Douglas Alem, 2023. "Practicable robust stochastic optimization under divergence measures with an application to equitable humanitarian response planning," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(3), pages 759-806, September.
  7. Ying-Qi Zhao & Michael R. Kosorok, 2014. "Discussion of combining biomarkers to optimize patient treatment recommendations," Biometrics, The International Biometric Society, vol. 70(3), pages 713-716, September.
  8. Christophe Denis & Charlotte Dion & Miguel Martinez, 2020. "Consistent procedures for multiclass classification of discrete diffusion paths," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 47(2), pages 516-554, June.
  9. Gérard Biau & Benoît Cadre & Quentin Paris, 2015. "Cox process functional learning," Statistical Inference for Stochastic Processes, Springer, vol. 18(3), pages 257-277, October.
  10. Steinwart, Ingo & Hush, Don & Scovel, Clint, 2009. "Learning from dependent observations," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 175-194, January.
  11. Xiaotong Shen & Lifeng Wang, 2007. "Discussion of ``2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization'' by V. Koltchinskii," Papers 0708.0121, arXiv.org.
  12. Nam Ho-Nguyen & Fatma Kılınç-Karzan, 2022. "Risk Guarantees for End-to-End Prediction and Optimization Processes," Management Science, INFORMS, vol. 68(12), pages 8680-8698, December.
  13. Rubin Daniel B. & van der Laan Mark J., 2012. "Statistical Issues and Limitations in Personalized Medicine Research with Clinical Trials," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-20, July.
  14. Aleksandar Arandjelovi'c & Julia Eisenberg, 2024. "Reinsurance with neural networks," Papers 2408.06168, arXiv.org.
  15. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2006. "Robust Learning from Bites for Data Mining," Technical Reports 2006,03, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
  16. Peter L. Bartlett & Shahar Mendelson, 2007. "Discussion of "2004 IMS Medallion Lecture: Local Rademacher complexities and oracle inequalities in risk minimization" by V. Koltchinskii," Papers 0708.0089, arXiv.org.
  17. Hable, Robert & Christmann, Andreas, 2011. "On qualitative robustness of support vector machines," Journal of Multivariate Analysis, Elsevier, vol. 102(6), pages 993-1007, July.
  18. Xiang Zhang & Yichao Wu & Lan Wang & Runze Li, 2016. "Variable selection for support vector machines in moderately high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(1), pages 53-76, January.
  19. Toru Kitagawa & Shosei Sakaguchi & Aleksey Tetenov, 2021. "Constrained Classification and Policy Learning," Papers 2106.12886, arXiv.org, revised Jul 2023.
  20. Jun-ya Gotoh & Stan Uryasev, 2017. "Support vector machines based on convex risk functions and general norms," Annals of Operations Research, Springer, vol. 249(1), pages 301-328, February.
  21. Pierre Alquier & Vincent Cottet & Guillaume Lecué, 2017. "Estimation bounds and sharp oracle inequalities of regularized procedures with Lipschitz loss functions," Working Papers 2017-30, Center for Research in Economics and Statistics.
  22. Yaoyao Xu & Menggang Yu & Ying‐Qi Zhao & Quefeng Li & Sijian Wang & Jun Shao, 2015. "Regularized outcome weighted subgroup identification for differential treatment effects," Biometrics, The International Biometric Society, vol. 71(3), pages 645-653, September.
  23. Hung Yi Lee & Charles Hernandez & Hongcheng Liu, 2023. "Regularized sample average approximation for high-dimensional stochastic optimization under low-rankness," Journal of Global Optimization, Springer, vol. 85(2), pages 257-282, February.
  24. Adam N. Elmachtoub & Paul Grigas, 2022. "Smart “Predict, then Optimize”," Management Science, INFORMS, vol. 68(1), pages 9-26, January.
  25. Zhiyu Ma & Shaowen Yao & Liwen Wu & Song Gao & Yunqi Zhang, 2022. "Hateful Memes Detection Based on Multi-Task Learning," Mathematics, MDPI, vol. 10(23), pages 1-16, November.
  26. Christmann, Andreas & Steinwart, Ingo & Hubert, Mia, 2007. "Robust learning from bites for data mining," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 347-361, September.
  27. Andrew Bennett & Nathan Kallus, 2020. "Efficient Policy Learning from Surrogate-Loss Classification Reductions," Papers 2002.05153, arXiv.org.
  28. Lee, Yoonkyung & Wang, Rui, 2015. "Does modeling lead to more accurate classification?: A study of relative efficiency in linear classification," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 232-250.
  29. Yanqing Wang & Ying‐Qi Zhao & Yingye Zheng, 2020. "Learning‐based biomarker‐assisted rules for optimized clinical benefit under a risk constraint," Biometrics, The International Biometric Society, vol. 76(3), pages 853-862, September.
  30. Lin, Xiefang & Fang, Fang, 2024. "Variable selection of Kolmogorov-Smirnov maximization with a penalized surrogate loss," Computational Statistics & Data Analysis, Elsevier, vol. 195(C).
  31. Luis M. Briceño-Arias & Giovanni Chierchia & Emilie Chouzenoux & Jean-Christophe Pesquet, 2019. "A random block-coordinate Douglas–Rachford splitting method with low computational complexity for binary logistic regression," Computational Optimization and Applications, Springer, vol. 72(3), pages 707-726, April.
  32. Gerard Kerkyacharian & Alexandre B. Tsybakov & Vladimir Temlyakov & Dominique Picard & Vladimir Koltchinskii, 2013. "Optimal Exponential Bounds on the Accuracy of Classification," Working Papers 2013-39, Center for Research in Economics and Statistics.
  33. Piotr Pokarowski & Wojciech Rejchel & Agnieszka Sołtys & Michał Frej & Jan Mielniczuk, 2022. "Improving Lasso for model selection and prediction," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 49(2), pages 831-863, June.
  34. Guillaume Lecu'e, 2007. "Suboptimality of Penalized Empirical Risk Minimization in Classification," Papers math/0703811, arXiv.org.
  35. Zhang, Chunming, 2010. "Statistical inference of minimum BD estimators and classifiers for varying-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1574-1593, August.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.