搜索结果: 1-15 共查到“管理学 kernel”相关记录50条 . 查询时间(0.103 秒)
Estimating Mixture of Gaussian Processes by Kernel Smoothing
Identifiability EM algorithm Kernel regression Gaussian process Functional principal component analysis
2016/1/26
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...
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.
Learning the Kernel via Convex Optimization
Convex optimization kernel methods machine learning support vector machine
2015/7/9
The performance of a kernel-based learning algorithm depends very much on the choice of the kernel. Recently, much attention has been paid to the problem of learning the kernel itself from given train...
Kernel-Based Reinforcement Learning in Average-Cost Problems
Average–cost problem dynamic programming kernel smoothing local averaging Markov decision process (MDP)
2015/7/8
Reinforcement learning (RL) is concerned with the identification of optimal controls in Markov decision processes (MDPs) where no explicit model of the transition probabilities is available. Many exis...
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...
Practical Tikhonov Regularized Estimators in Reproducing Kernel Hilbert Spaces for Statistical Inverse Problems
Tikhonov Regularized Estimators Reproducing Kernel Hilbert Spaces Statistical Inverse Problems
2013/6/13
Regularized kernel methods such as support vector machines (SVM) and support vector regression (SVR) constitute a broad and flexible class of methods which are theoretically well investigated and comm...
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...
Variational Semi-blind Sparse Deconvolution with Orthogonal Kernel Bases and its Application to MRFM
Variational Bayesian inference posterior image distribution image reconstruction hyperparameter estimation MRFM experiment
2013/5/2
We present a variational Bayesian method of joint image reconstruction and point spread function (PSF) estimation when the PSF of the imaging device is only partially known. To solve this semi-blind d...
On a link between kernel mean maps and Fraunhofer diffraction, with an application to super-resolution beyond the diffraction limit
On a link between kernel mean maps Fraunhofer diffraction an application super-resolution beyond the diffraction limit
2013/4/28
We establish a link between Fourier optics and a recent construction from the machine learning community termed the kernel mean map. Using the Fraunhofer approximation, it identifies the kernel with t...
基于Multi-kernel 和KRR的数据还原算法
数据还原 核岭回归 迭代 超高维欧氏空间
2014/7/21
由于数据被核化后不能还原, 使核方法的应用受到局限. 对此, 提出一种基于Multi-kernel 和KRR的数据还原算法. 首先, 通过同类数据中已知数据进行多次核化迭代, 使已知数据在超高维欧氏空间中呈线性; 然后, 利用已知数据对同类未知数据进行线性表示, 并以Kernel ridge regression (KRR) 算法进行未知数据的回归; 最后实现数据还原. 选取Iris flower...
A comparative study of new cross-validated bandwidth selectors for kernel density estimation
kernel density estimation data-adaptive bandwidth selection indirect cross-validation do-validation.
2012/11/22
Recent contributions to kernel smoothing show that the performance of cross-validated bandwidth selectors improve significantly from indirectness. Indirect crossvalidation first estimates the classica...
A note on extreme values and kernel estimators of sample boundaries
support estimation asymptotic normality kernel estimator ex-treme values.
2012/9/18
In a previous paper [3], we studied a kernel estimate of the upper edge of a two-dimensional bounded set, based upon the extreme values of a Poisson point process. The initial paper [1] on the subject...