5. Randomized estimators and empirical complexity

Chapter
Part of the Lecture Notes in Mathematics book series (LNM, volume 1851)

Abstract

  • 5.1. A pseudo-Bayesian approach to adaptive inference
    • 5.1.1. General framework

    • 5.1.2. Some pervading ideas

  • 5.2. A randomized rule for pattern recognition

  • 5.3. Generalizations of theorem 5.2.3

  • 5.4. The non-ambiguous case

  • 5.5. Empirical complexity bounds for the Gibbs estimator

  • 5.6. Non randomized classification rules

  • 5.7. Application to classification trees

  • 5.8. The regression setting

  • 5.9. Links with penalized least square regression
    • 5.9.1. The general case

    • 5.9.2. Adaptive linear least square regression estimation

  • 5.10. Some elementary bounds

  • 5.11. Some refinements about the linear regression case

Mathematics Subject Classification (2000):

62B10 68T05 62C05 62E17 62G05 62G07 62G08 62H30 62J02 94A15 94A17 94A24 68Q32 60F10 60J10 60J20 65C05 68W20 

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Copyright information

© Springer-Verlag Berlin/Heidelberg 2004

Authors and Affiliations

  1. 1.Laboratoire de Probabilités et Modèles Aléatoires, UMR CNRS 7599, Case 188Université Paris 6Paris Cedex 05France

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