bmrm: Bundle Methods for Regularized Risk Minimization Package. Bundle methods for minimization of convex and non-convex risk under L1 or L2 regularization. Implements the algorithm proposed by Teo et al. (JMLR 2010) as well as the extension proposed by Do and Artieres (JMLR 2012). The package comes with lot of loss functions for machine learning which make it powerful for big data analysis. The applications includes: structured prediction, linear SVM, multi-class SVM, f-beta optimization, ROC optimization, ordinal regression, quantile regression, epsilon insensitive regression, least mean square, logistic regression, least absolute deviation regression (see package examples), etc... all with L1 and L2 regularization.

References in zbMATH (referenced in 23 articles )

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  1. Brust, Johannes J.; Di, Zichao (Wendy); Leyffer, Sven; Petra, Cosmin G.: Compact representations of structured BFGS matrices (2021)
  2. Chen, Liang; Li, Xudong; Sun, Defeng; Toh, Kim-Chuan: On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming (2021)
  3. Moazeni, Somayeh; Collado, Ricardo A.: Resource allocation for contingency planning: an inexact proximal bundle method for stochastic optimization (2021)
  4. Montañés, Diana C.; Quiroz, Adolfo J.; Dulce Rubio, Mateo; Riascos Villegas, Alvaro J.: Efficient nearest neighbors methods for support vector machines in high dimensional feature spaces (2021)
  5. James, Gareth M.; Paulson, Courtney; Rusmevichientong, Paat: Penalized and constrained optimization: an application to high-dimensional website advertising (2020)
  6. Asi, Hilal; Duchi, John C.: Stochastic (approximate) proximal point methods: convergence, optimality, and adaptivity (2019)
  7. Piccialli, Veronica; Sciandrone, Marco: Nonlinear optimization and support vector machines (2018)
  8. Wang, Mengdi: Vanishing price of decentralization in large coordinative nonconvex optimization (2017)
  9. Antoniuk, Kostiantyn; Franc, Vojtěch; Hlaváč, Václav: V-shaped interval insensitive loss for ordinal classification (2016)
  10. Cheng, Fan; Zhou, Yuan; Gao, Jian; Zheng, Shuangqiu: Efficient optimization of (F)-measure with cost-sensitive SVM (2016)
  11. Nagesseur, Ludovic: A bundle method using two polyhedral approximations of the (\epsilon)-enlargement of a maximal monotone operator (2016)
  12. Tibshirani, Ryan J.: A general framework for fast stagewise algorithms (2015)
  13. Lee, Ching-Pei; Lin, Chih-Jen: Large-scale linear rankSVM (2014)
  14. Zhang, Xinhua; Saha, Ankan; Vishwanathan, S. V. N.: Accelerated training of max-margin Markov networks with kernels (2014)
  15. Bach, Francis: Learning with submodular functions: a convex optimization perspective (2013)
  16. Carrizosa, Emilio; Romero Morales, Dolores: Supervised classification and mathematical optimization (2013)
  17. Daumé, Hal III; Phillips, Jeff M.; Saha, Avishek; Venkatasubramanian, Suresh: Efficient protocols for distributed classification and optimization (2012)
  18. Do, Trinh Minh Tri; Artières, Thierry: Regularized bundle methods for convex and non-convex risks (2012)
  19. Lin, Huiling: An inexact spectral bundle method for convex quadratic semidefinite programming (2012)
  20. Airola, Antti; Pahikkala, Tapio; Waegeman, Willem; De Baets, Bernard; Salakoski, Tapio: An experimental comparison of cross-validation techniques for estimating the area under the ROC curve (2011)

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