搜索结果: 1-15 共查到“理论统计学 kernel”相关记录32条 . 查询时间(0.022 秒)
Estimating Mixture of Gaussian Processes by Kernel Smoothing
Identifiability EM algorithm Kernel regression Gaussian process Functional principal component analysis
2016/1/20
When the functional data are not homogeneous, e.g., there exist multiple classes of func-tional curves in the dataset, traditional estimation methods may fail. In this paper, we propose a new estimati...
Support Vector Machines,Kernel Logistic Regression,and Boosting
Support Vector Machines Kernel Logistic Regression Boosting
2015/8/21
Support Vector Machines,Kernel Logistic Regression,and Boosting.
Limit theorems for kernel density estimators under dependent samples
Kernel density estimator consistency convergence rate mixing rate
2013/6/14
In this paper, we construct a moment inequality for mixing dependent random variables, it is of independent interest. As applications, the consistency of the kernel density estimation is investigated....
Divide and Conquer Kernel Ridge Regression: A Distributed Algorithm with Minimax Optimal Rates
Divide and Conquer Kernel Ridge Regression A Distributed Algorithm Minimax Optimal Rates
2013/6/14
We establish optimal convergence rates for a decomposition-based scalable approach to kernel ridge regression. The method is simple to describe: it randomly partitions a dataset of size N into m subse...
Embedding Riemannian Manifolds by the Heat Kernel of the Connection Laplacian
Embedding Riemannian Manifolds Heat Kernel Connection Laplacian
2013/6/17
Given a class of closed Riemannian manifolds with prescribed geometric conditions, we introduce an embedding of the manifolds into $\ell^2$ based on the heat kernel of the Connection Laplacian associa...
Probit transformation for kernel density estimation on the unit interval
transformation kernel density estimator boundary bias local likelihood density estimation local log-polynomial density estimation
2013/4/27
Kernel estimation of a probability density function supported on the unit interval has proved difficult, because of the well known boundary bias issues a conventional kernel density estimator would ne...
We propose a method for nonparametric density estimation that exhibits robustness to contamination of the training sample. This method achieves robustness by combining a traditional kernel density est...
Geometric Allocation Approach for Transition Kernel of Markov Chain
Markov chain Transition kernel Geometric allocation
2011/7/7
We introduce a new geometric approach that constructs a transition kernel of Markov chain. Our method always minimizes the average rejection rate and even reduce it to zero in many relevant cases, whi...
Sequential Monte Carlo (SMC) approaches have become work horses in approximate Bayesian computation (ABC). Here we discuss how to construct the perturbation kernels that are required in ABC-SMC approa...
Online Multiple Kernel Learning for Structured Prediction
Online Multiple Kernel Learning r Structured Prediction
2010/10/19
Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning...
Nonparametric kernel estimation of the probability density function of regression errors using estimated residuals
Kernel density estimation Leave-one-out kernel estimator Two-steps estimator
2010/10/14
This paper deals with the nonparametric density estimation of the regression error term assuming its independence with the covariate. The difference between the feasible estimator which uses the estim...
Minimax Robust Function Approximation in Reproduction Kernel Hilbert Spaces
RKHS Thin-Plate Splines Smoothing Splines Scattered Data Interpolation and Approximation
2010/4/30
In this paper, we present a unified approach to function approximation in reproducing kernel Hilbert spaces (RKHS) that establishes a previously unrecognized optimality property for several well-known...
Maxiset in sup-norm for kernel estimators。
Kernel methods and minimum contrast estimators for empirical deconvolution
bandwidth inverse problems kernel estimators local linearmethods local polynomial methods minimum contrast methods
2010/3/11
We survey classical kernel methods for providing nonparametric solutions
to problems involving measurement error. In particular we outline
kernel-basedmethodology in this setting, and discuss its ba...
Classifying Network Data with Deep Kernel Machines
deep architecture diffusion kernel kernel density estimation nearest centroid socialnetwork support vector machine
2010/3/9
Inspired by a growing interest in analyzing network data, we study the problem of node classifi-
cation on graphs, focusing on approaches based on kernel machines. Conventionally, kernel machines
ar...