@article{AIHPB_1999__35_6_793_0, author = {Blanchard, Gilles}, title = {The {\textquotedblleft}progressive mixture{\textquotedblright} estimator for regression trees}, journal = {Annales de l'I.H.P. Probabilit\'es et statistiques}, pages = {793--820}, publisher = {Gauthier-Villars}, volume = {35}, number = {6}, year = {1999}, mrnumber = {1725711}, zbl = {1054.62539}, language = {en}, url = {http://www.numdam.org/item/AIHPB_1999__35_6_793_0/} }
TY - JOUR AU - Blanchard, Gilles TI - The “progressive mixture” estimator for regression trees JO - Annales de l'I.H.P. Probabilités et statistiques PY - 1999 SP - 793 EP - 820 VL - 35 IS - 6 PB - Gauthier-Villars UR - http://www.numdam.org/item/AIHPB_1999__35_6_793_0/ LA - en ID - AIHPB_1999__35_6_793_0 ER -
Blanchard, Gilles. The “progressive mixture” estimator for regression trees. Annales de l'I.H.P. Probabilités et statistiques, Tome 35 (1999) no. 6, pp. 793-820. http://www.numdam.org/item/AIHPB_1999__35_6_793_0/
[1] Shape quantization and recognition with randomized trees, Neural Computation 9 (1997) 1545-1588.
and ,[2] Joint induction of shape features and tree classifiers, IEEE Trans. PAMI 19 (11) (1997) 1300-1306.
, and ,[3] Information theoretic determination of minimax rates of convergence, Department of Statistics, Yale University, 1997.
and ,[4] Are Bayes rules consistent in information? in: T.M. Cover and B. Gopinath (Eds.), Open Problems in Communication and Computation, Springer, Berlin, 1987, pp. 85-91.
,[5] Approximation dans les espaces métriques et théorie de l'approximation, Z. Wahrscheinlichkeitstheor. Verw. Geb. 65 (1983) 181-237. | MR | Zbl
,[6] "Universal" aggregation rules with exact bias bounds, Preprint of the Laboratoire de Probabilités et Modèles Aléatoires, Université Pierre et Marie Curie, available at http://www.proba.jussieu.fr/mathdoc/preprints/index.html#1999 (to appear in Annals of Statistics), 1999.
,[7] Bayesian CART model search, JASA 93 (1998) 935-947.
, and ,[8] Elements of Information Theory, Wiley Series in Telecommunications, Wiley, New York, 1991. | MR | Zbl
and ,[9] Nonparametric Density Estimation: The L1 View, Wiley, New York, 1985. | MR | Zbl
and ,[10] Predicting nearly as well as the best pruning of a decision tree, Machine Learning 27 (1997) 51-68.
and ,[11] The context-tree weighting method: basic properties, IEEE Trans. Inform. Theory 41 (3) (1995) 653-664. | Zbl
, and ,[12] Context weighting for general finite-context sources, IEEE Trans. Inform. Theory 42 (5) (1996) 1514- 1520. | Zbl
, and ,