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2 We consider hierarchical mixtures-of-experts (HME) models where exponential family regression models with generalized linear mean functions of the form (+ x T) are mixed. Here () is the inverse… (More)

- Wenxin Jiang, Martin A. Tanner
- Neural Networks
- 1999

In mixtures-of-experts (ME) models, "experts" of generalized linear models are combined, according to a set of local weights called the "gating function". The invariant transformations of the ME… (More)

- Wenxin Jiang, Martin A. Tanner
- IEEE Trans. Information Theory
- 2000

| In the class of hierarchical mixtures-of-experts (HME) models, \experts" in the exponential family with generalized linear mean functions of the form (+ x T) are mixed, according to a set of local… (More)

- Wenxin Jiang
- 2000

Recent experiments and theoretical studies show that AdaBoost can over t in the limit of large time. If running the algorithm forever is suboptimal, a natural question is how low can the prediction… (More)

- Wenxin Jiang, Martin A. Tanner
- Neural Computation
- 1999

We investigate a class of hierarchical mixtures-of-experts (HME) models where generalized linear models with nonlinear mean functions of the form ( xT) are mixed. Here () is the inverse link… (More)

- Wenxin Jiang
- 2006

Bayesian variable selection has gained much empirical success recently in a variety of applications when the number K of explanatory variables (x1, . . . , xK) is possibly much larger than the sample… (More)

- Daniel B. Stouffer, J. Camacho, Wenxin Jiang, Luis A. Nunes Amaral
- Proceedings. Biological sciences
- 2007

Food webs aim to provide a thorough representation of the trophic interactions found in an ecosystem. The complexity of empirical food webs, however, is leading many ecologists to focus dynamic… (More)

- Wenxin Jiang
- 2000

In this paper we propose Bayesian and frequentist approaches to ecological inference, based on R C contingency tables, including a covariate. The proposed Bayesian model extends the binomial-beta… (More)

In the popular approach of “Bayesian variable selection” (BVS), one uses prior and posterior distributions to select a subset of candidate variables to enter the model. A completely new direction… (More)

- Wenxin Jiang
- Journal of Machine Learning Research
- 2009

The statistical learning theory of risk minimization depen ds heavily on probability bounds for uniform deviations of the empirical risks. Classical probabil ity bounds using Hoeffding’s inequality… (More)