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最新文章

• The golden chain - Discussion AI翻译
  作者：Bühlmann, P; Yu, B
  作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich; University of California System; University of California Berkeley
  摘要：Jiang, Lugosi and Vayatis, and Zhang ought to be congratulated for their different works on the original AdaBoost algorithm with early stopping (Jiang), an l(1)-penalized version of boosting (Lugosi and Vayatis) and a convex minimization method which can be viewed as an l(2)-penalized version of boosting (Zhang).
• Three papers on boosting: An introduction AI翻译
  作者：Koltchinskii, V; Yu, B
  作者单位：University of New Mexico; University of California System; University of California Berkeley
• On the Bayes-risk consistency of regularized boosting methods AI翻译
  作者：Lugosi, G; Vayatis, N
  作者单位：Pompeu Fabra University; Sorbonne Universite; Universite Paris Cite
  摘要：The probability of error of classification methods based on convex combinations of simple base classifiers by boosting algorithms is investigated. The main result of the paper is that certain regularized boosting algorithms provide Bayes-risk consistent classifiers under the sole assumption that the Bayes classifier may be approximated by a convex combination of the base classifiers. Nonasymptotic distribution-free bounds are also developed which offer interesting new insight into how boosting...
• On the forward and backward algorithms of projection pursuit AI翻译
  作者：Zhu, M
  作者单位：University of Waterloo
  摘要：This article provides a historic review of the forward and backward projection pursuit algorithms, previously thought to be equivalent, and points out an important difference between the two. In doing so, a small error in the original exploratory projection pursuit article by Friedman [J Amer. Statist. Assoc. 82 (1987) 249-266] is corrected. The implication of the difference is briefly discussed in the context of an application in which projection pursuit density estimation is used as a buildi...
• The golden chain - Discussion AI翻译
  作者：Friedman, J; Hastie, T; Rosset, S; Tibshirani, R; Zhu, J
  作者单位：Stanford University
• The golden chain - Rejoinder AI翻译
  作者：Jiang, WX
  作者单位：Northwestern University
• Influence function and maximum bias of projection depth based estimators AI翻译
  作者：Zuo, YJ; Cui, HJ; Young, D
  作者单位：Michigan State University; Beijing Normal University; Arizona State University; Arizona State University-Tempe
  摘要：Location estimators induced from depth functions increasingly have been pursued and studied in the literature. Among them are those induced from projection depth functions. These projection depth based estimators have favorable properties among their competitors. In particular, they possess the best possible finite sample breakdown point robustness. However, robustness of estimators cannot be revealed by the finite sample breakdown point alone. The influence function, gross error sensitivity, ...
• Statistical behavior and consistency of classification methods based on convex risk minimization - Discussion AI翻译
  作者：Bartlett, PL; Jordan, MI; McAuliffe, JD
  作者单位：University of California System; University of California Berkeley; University of California System; University of California Berkeley
• The golden chain - Discussion AI翻译
  作者：Freund, Y; Schapire, RE
  作者单位：Columbia University; Princeton University
• Optimality of the CUSUM procedure in continuous time AI翻译
  作者：Moustakides, GV
  作者单位：Universite de Rennes; University of Thessaly
  摘要：The optimality of CUSUM under a Lorden-type criterion setting is considered. We demonstrate the optimality of the CUSUM test for lto processes, in a sense similar to Lorden's, but with a criterion that replaces expected delays by the corresponding Kullback-Leibler divergence.