#### Filter Results:

- Full text PDF available (18)

#### Publication Year

1995

2017

- This year (2)
- Last five years (34)

#### Publication Type

#### Co-author

#### Publication Venue

#### Key Phrases

Learn More

- Yuhong Yang, Andrew Barron
- 1995

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. Institute of… (More)

- Yuhong Yang, Andrew R. Barron
- IEEE Trans. Information Theory
- 1998

—Probability models are estimated by use of penalized log-likelihood criteria related to AIC and MDL. The accuracies of the density estimators are shown to be related to the trade-off between three terms: the accuracy of approximation, the model dimension, and the descriptive complexity of the model classes. The asymptotic risk is determined under… (More)

- Yuhong Yang
- 2006

We address the consistency property of cross validation (CV) for classification. Sufficient conditions are obtained on the data splitting ratio to ensure that the better classifier between two candidates will be favored by CV with probability approaching 1. Interestingly, it turns out that for comparing two general learning methods, the ratio of the… (More)

- Yuhong Yang
- IEEE Trans. Information Theory
- 1999

— This paper studies minimax aspects of nonpara-metric classification. We first study minimax estimation of the conditional probability of a class label, given the feature variable. This function, say f, is assumed to be in a general nonparametric class. We show the minimax rate of convergence under square L 2 loss is determined by the massiveness of the… (More)

- Yuhong Yang
- IEEE Trans. Information Theory
- 1999

|We study nonparametric estimation of a conditional probability for classiication based on a collection of nite-dimensional models. For the sake of exibility, diierent types of models, linear or nonlinear, are allowed as long as each satisses a dimensionality assumption. We show that with a suitable model selection criterion, the penalized maximum… (More)

- Wei Qian, Yuhong Yang
- 2012

The adaptive lasso is a model selection method shown to be both consistent in variable selection and asymptotically normal in coefficient estimation. The actual variable selection performance of the adaptive lasso depends on the weight used. It turns out that the weight assignment using the OLS estimate (OLS-adaptive lasso) can result in very poor… (More)

- Yuhong Yang
- AISTATS
- 2007

Efforts have been directed at obtaining flexible learning procedures that optimally adapt to various possible characteristics of the data generating mechanism. A question that addresses the issue of how far one can go in this direction is: Given a regression procedure, however sophisticated it is, how many regression functions are estimated accurately? In… (More)

- Yuhong Yang
- IEEE Trans. Information Theory
- 2001

|We study minimax-rate adaptive estimation for density classes indexed by continuous hyper-parameters. The classes are assumed to be partially ordered in terms of inclusion relationship. Under a mild condition on the minimax risks, we show that a minimax-rate adaptive estimator can be constructed for the classes. 1 Problem of interest This paper concerns… (More)

- Shi Dong, Ruimin Hu, Xiaochen Wang, Yuhong Yang, Weiping Tu
- EURASIP J. Audio, Speech and Music Processing
- 2014

- Fuchang Gao, Ching-Kang Ing, Yuhong Yang
- Journal of Approximation Theory
- 2013

Consider q-hulls, 0 < q ≤ 1, from a dictionary of M functions in L p space for 1 ≤ p < ∞. Their precise metric entropy orders are derived. Sparse linear approximation bounds are obtained to characterize the number of terms needed to achieve accurate approximation of the best function in a q-hull that is closest to a target function. Furthermore, in the… (More)