4期

最新文章

• Statistical properties of the method of regularization with periodic Gaussian reproducing kernel AI翻译
  作者：Lin, Y; Brown, LD
  作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Pennsylvania
  摘要：The method of regularization with the Gaussian reproducing kernel is popular in the machine learning literature and successful in many practical applications. In this paper we consider the periodic version of the Gaussian kernel regularization. We show in the white noise model setting, that in function spaces of very smooth functions, such as the infinite-order Sobolev space and the space of analytic functions, the method under consideration is asymptotically minimax; in finite-order Sobolev s...
• Generalization bounds for averaged classifiers AI翻译
  作者：Freund, Y; Mansour, Y; Schapire, RE
  作者单位：Columbia University; Tel Aviv University; Princeton University
  摘要：We study a simple learning algorithm for binary classification. Instead of predicting with the best hypothesis in the hypothesis class, that is, the hypothesis that minimizes the training error, our algorithm predicts with a weighted average of all hypotheses, weighted exponentially with respect to their training error. We show that the prediction of this algorithm is much more stable than the prediction of an algorithm that predicts with the best hypothesis. By allowing the algorithm to absta...
• Complexity regularization via localized random penalties AI翻译
  作者：Lugosi, G; Wegkamp, M
  作者单位：Pompeu Fabra University; State University System of Florida; Florida State University
  摘要：In this article, model selection via penalized empirical loss minimization in nonparametric classification problems is studied. Data-dependent penalties are constructed, which are based on estimates of the complexity of a small subclass of each model class, containing only those functions with small empirical loss. The penalties are novel since those considered in the literature are typically based on the entire model class. Oracle inequalities using these penalties are established, and the ad...
• Construction of E(s2)-optimal supersaturated designs AI翻译
  作者：Bulutoglu, DA; Cheng, CS
  作者单位：United States Department of Defense; United States Air Force; US Air Force Research Laboratory; Air Force Institute of Technology (AFIT); Academia Sinica - Taiwan
  摘要：Booth and Cox proposed the E(s(2)) criterion for constructing two-level supersaturated designs. Nguyen [Technometrics 38 (1996) 69-73] and Tang and Wu [Canad. J. Statist 25 (1997) 191-201] independently derived a lower bound for E(s(2)). This lower bound can be achieved only when m is a multiple of N - 1, where m is the number of factors and N is the run size. We present a method that uses difference families to construct designs that satisfy this lower bound. We also derive better lower bound...
• Convergence rates for posterior distributions and adaptive estimation AI翻译
  作者：Huang, TM
  作者单位：Iowa State University
  摘要：The goal of this paper is to provide theorems on convergence rates of posterior distributions that can be applied to obtain good convergence rates in the context of density estimation as well as regression. We show how to choose priors so that the posterior distributions converge at the optimal rate without prior knowledge of the degree of smoothness of the density function or the regression function to be estimated.
• Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences AI翻译
  作者：Johnstone, IM; Silverman, BW
  作者单位：Stanford University; University of Oxford
  摘要：An empirical Bayes approach to the estimation of possibly sparse sequences observed in Gaussian white noise is set out and investigated. The prior considered is a mixture of an atom of probability at zero and a heavy-tailed density gamma, with the mixing weight chosen by marginal maximum likelihood, in the hope of adapting between sparse and dense sequences. If estimation is then carried Out using the posterior median, this is a random thresholding procedure. Other thresholding rules employing...
• Statistical estimation in the proportional hazards model with risk set sampling AI翻译
  作者：Chen, K
  作者单位：Hong Kong University of Science & Technology
  摘要：Thomas' partial likelihood estimator of regression parameters is widely used in the analysis of nested case-control data with Cox's model. This paper proposes a new estimator of the regression parameters, which is consistent and asymptotically normal. Its asymptotic variance is smaller than that of Thomas' estimator away from the null. Unlike some other existing estimators, the proposed estimator does not rely on any more data than strictly necessary for Thomas' estimator and is easily computa...