Aggregated estimators and empirical complexity for least square regression

Jean-Yves Audibert

Annales de l'I.H.P. Probabilités et statistiques (2004)

  • Volume: 40, Issue: 6, page 685-736
  • ISSN: 0246-0203

How to cite

top

Audibert, Jean-Yves. "Aggregated estimators and empirical complexity for least square regression." Annales de l'I.H.P. Probabilités et statistiques 40.6 (2004): 685-736. <http://eudml.org/doc/77830>.

@article{Audibert2004,
author = {Audibert, Jean-Yves},
journal = {Annales de l'I.H.P. Probabilités et statistiques},
keywords = {Deviation inequalities; Adaptive estimator; Oracle inequalities; Boosting; Bayesian expected risk bound; minimax bounds; binary classification},
language = {eng},
number = {6},
pages = {685-736},
publisher = {Elsevier},
title = {Aggregated estimators and empirical complexity for least square regression},
url = {http://eudml.org/doc/77830},
volume = {40},
year = {2004},
}

TY - JOUR
AU - Audibert, Jean-Yves
TI - Aggregated estimators and empirical complexity for least square regression
JO - Annales de l'I.H.P. Probabilités et statistiques
PY - 2004
PB - Elsevier
VL - 40
IS - 6
SP - 685
EP - 736
LA - eng
KW - Deviation inequalities; Adaptive estimator; Oracle inequalities; Boosting; Bayesian expected risk bound; minimax bounds; binary classification
UR - http://eudml.org/doc/77830
ER -

References

top
  1. [1] L. Breiman, Bagging predictors, Machine Learning24 (2) (1996) 123-140. Zbl0858.68080
  2. [2] L. Breiman, Arcing classifiers, Ann. Statist26 (3) (1998) 801-849. Zbl0934.62064MR1635406
  3. [3] O. Catoni, Statistical Learning Theory and Stochastic Optimization, in: Probability Summer School, Saint Flour, 2001, Springer-Verlag, submitted for publication. Zbl1076.93002MR2163920
  4. [4] Y. Freund, R.E. Schapire, Experiments with a new boosting algorithm, in: Machine Learning: Proceedings of the Thirteenth International Conference, 1996, pp. 148-156. 
  5. [5] J. Friedman, T. Hastie, R. Tibshirani, Additive logistic regression: a statistical view of boosting, Technical Report, Dept. of Statistics, Stanford University, 1998. Zbl1106.62323
  6. [6] A. Juditsky, A. Nemirovski, Functional aggregation for nonparametric estimation, Ann. Statist28 (2000) 681-712. Zbl1105.62338MR1792783
  7. [7] E. Mammen, A.B. Tsybakov, Smooth discrimination analysis, Ann. Statist27 (1999) 1808-1829. Zbl0961.62058MR1765618
  8. [8] D.A. McAllester, PAC-bayesian stochastic model selection, Machine Learning J (2001), submitted for publication. Zbl1056.68122
  9. [9] A. Nemirovski, Lectures on Probability Theory and Statistics. Part II: Topics in Non-Parametric Statistics, in: Probability Summer School, Saint Flour, Springer-Verlag, Berlin, 1998. Zbl0998.62033MR1775640
  10. [10] G. Rätsch, M. Warmuth, S. Mika, T. Onoda, S. Lemm, K.-R. Müller, Barrier boosting, in: Proc. COLT'00, Morgan Kaufmann, Palo Alto, 2000, pp. 170-179. 
  11. [11] R.E. Schapire, Y. Singer, Improved boosting algorithms using confidence-rated predictions, 1998, pp. 80–91. Zbl0945.68194
  12. [12] A.B. Tsybakov, Optimal aggregation of classifiers in statistical learning, 2001. Zbl1105.62353
  13. [13] Y. Yang, Aggregating regression procedures for a better performance, 2001. 

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

                
Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.