• Publications
  • Influence
Model selection and estimation in regression with grouped variables
Summary.  We consider the problem of selecting grouped variables (factors) for accurate prediction in regression. Such a problem arises naturally in many practical situations with the multifactor
Model selection and estimation in the Gaussian graphical model
TLDR
The implementation of the penalized likelihood methods for estimating the concentration matrix in the Gaussian graphical model is nontrivial, but it is shown that the computation can be done effectively by taking advantage of the efficient maxdet algorithm developed in convex optimization.
Component selection and smoothing in multivariate nonparametric regression
TLDR
A detailed analysis reveals that the COSSO does model selection by applying a novel soft thresholding type operation to the function components, which leads naturally to an iterative algorithm.
Multicategory Support Vector Machines
TLDR
The MSVM is proposed, which extends the binary SVM to the multicategory case and has good theoretical properties, and an approximate leave-one-out cross-validation function is derived, analogous to the binary case.
Support Vector Machines and the Bayes Rule in Classification
  • Yi Lin
  • Computer Science
    Data Mining and Knowledge Discovery
  • 1 July 2002
TLDR
It is shown that the asymptotic target of SVMs are some interesting classification functions that are directly related to the Bayes rule, and helps understand the success of SVM in many classification studies, and makes it easier to compare SVMs and traditional statistical methods.
Random Forests and Adaptive Nearest Neighbors
TLDR
It is shown that random forests with adaptive splitting schemes assign weights to k-PNNs in a desirable way: for the estimation at a given target point, these random forests assign voting weights to the k- PNNs of the target point according to the local importance of different input variables.
On the non‐negative garrotte estimator
Summary.  We study the non‐negative garrotte estimator from three different aspects: consistency, computation and flexibility. We argue that the non‐negative garrotte is a general procedure that can
Component selection and smoothing in smoothing spline analysis of variance models -- COSSO
TLDR
A detailed analysis reveals that the COSSO applies a novel soft thresholding type operation to the function components and selects the correct model structure with probability tending to one in the special case of a tensor product design with periodic functions.
Support Vector Machines for Classification in Nonstandard Situations
TLDR
This paper explains why the standard support vectors machine is not suitable for the nonstandard situation, and introduces a simple procedure for adapting the support vector machine methodology to the non standard situation.
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