Consistency of multiclass empirical risk minimization methods based on convex loss

被引:0
|
作者
Department of Mathematics, LMIB, Beijing University of Aeronautics and Astronautics, Beijing 100083, China [1 ]
不详 [2 ]
机构
来源
J. Mach. Learn. Res. | 2006年 / 2435-2447期
关键词
13;
D O I
暂无
中图分类号
学科分类号
摘要
引用
收藏
相关论文
共 50 条
  • [11] On the consistency of multiclass classification methods
    Tewari, Ambuj
    Bartlett, Peter L.
    JOURNAL OF MACHINE LEARNING RESEARCH, 2007, 8 : 1007 - 1025
  • [12] On the consistency of multiclass classification methods
    Tewari, A
    Bartlett, PL
    LEARNING THEORY, PROCEEDINGS, 2005, 3559 : 143 - 157
  • [13] Empirical Risk Minimization for Variable Consistency Dominance-Based Rough Set Approach
    Blaszczynski, Jerzy
    Kusunoki, Yoshifumi
    Inuiguchi, Masahiro
    Slowinski, Roman
    ROUGH SETS, FUZZY SETS, DATA MINING, AND GRANULAR COMPUTING, RSFDGRC 2015, 2015, 9437 : 63 - 72
  • [14] On the optimality of the empirical risk minimization procedure for the Convex aggregation problem
    Lecue, Guillaume
    Mendelson, Shahar
    ANNALES DE L INSTITUT HENRI POINCARE-PROBABILITES ET STATISTIQUES, 2013, 49 (01): : 288 - 306
  • [15] Convex risk minimization via proximal splitting methods
    Bot, Radu Ioan
    Hendrich, Christopher
    OPTIMIZATION LETTERS, 2015, 9 (05) : 867 - 885
  • [16] Convex risk minimization via proximal splitting methods
    Radu Ioan Boţ
    Christopher Hendrich
    Optimization Letters, 2015, 9 : 867 - 885
  • [17] A new analytical approach to consistency and overfitting in regularized empirical risk minimization
    Trillos, Nicolas Garcia
    Murray, Ryan
    EUROPEAN JOURNAL OF APPLIED MATHEMATICS, 2017, 28 (06) : 886 - 921
  • [18] On robustness properties of convex risk minimization methods for pattern recognition
    Christmann, A
    Steinwart, I
    JOURNAL OF MACHINE LEARNING RESEARCH, 2004, 5 : 1007 - 1034
  • [19] Convex Calibration Dimension for Multiclass Loss Matrices
    Ramaswamy, Harish G.
    Agarwal, Shivani
    JOURNAL OF MACHINE LEARNING RESEARCH, 2016, 17
  • [20] Differentially Private Empirical Risk Minimization with Smooth Non-Convex Loss Functions: A Non-Stationary View
    Wang, Di
    Xu, Jinhui
    THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, 2019, : 1182 - 1189