回归估计是统计学中基本问题之一。
Problems of regression function estimate is a basic problem in statistical theory.
通常情况下，回归估计的效率高于系统抽样。
The efficiency of regression estimate was usually higher than systematic sampling.
本文在群体相关模型下给出了一种新的回归估计。
In this paper, we give a new regression estimator under the cluster correlation model.
论文主要研究了非参数局部多项式回归估计模型。
This paper mainly studies the nonparametric regression estimation model of local polynomial.
结果表明SORR优于标准的支持向量机回归估计算法。
The experimental results show that the proposed SORR algorithm is better than the normal regression estimation algorithm of SVM.
在模式识别、回归估计、概率密度函数估计等方面都有应用。
In pattern recognition, regression estimates, the estimated probability density function, and other aspects of application.
本文将使用相对于最小二乘法更具有稳健性的分位点回归估计法。
So we introduced and used quantile regression method, which was robust in this situation.
目前，主要应用在模式识别、回归估计、概率密度函数估计等方面。
Now it is applied in Pattern Recognition, regression estimate and probability estimate.
目前，如何设计快速有效的回归估计算法仍然是支持向量机实际应用中的问题之一。
Now, how to design fast and efficient SVM algorithms applied to regression estimation becomes a great challenge in practical applications of support vector machine.
首先，从产生背景入手，介绍了非参数局部多项式回归估计模型的基本概念以及研究概况。
Firstly, we introduce the research situation of the local polynomial regression estimation and its basic concept from the model's background.
提出了无偏回归估计方法，从理论上证明了该法比常规的估计法具有更小的平均估计误差。
It was proven theoretically that the estimate errors of unbiased regression estimation are less than that of general regression estimation.
本文基于非参数核密度估计与核回归估计的基础上，介绍了合理选取核函数及窗宽的原则和方法。
This paper introduced the selection principle and method about a reasonable kernel function and bandwidth based on the nonparametric kernel density estimation and kernel regression estimation.
提出一种新的在线逆散射方法—支持向量机，通过支持向量机将原问题转化成一个回归估计问题。
In this paper, a new online methodology is presented for the solution of inverse scattering problems.
支撑矢量机是一种普适的算法，已经广泛地用于模式识别、回归估计、函数逼近、密度估计等方面。
SVM is a kind of general learning algorithms, which has been widely used in pattern recognition, regression estimation, function approximation, density estimation, etc.
建议的模型校正估计可以处理任何线性或非线性的工作模型，且在线性模型的情形下变为广义回归估计。
The proposed model-calibration estimators can handle any linear or nonlinear working models and reduce to the generalized regression estimators in the linear model case.
对有限总体在不放回抽样的方法下，构造了一类非线性回归估计量，讨论了它的性质及其有关抽样误差．。
A kind of nonlinear regression estimator for limited Population is given and the quality and the sampling variance about is discussed.
该文对用于回归估计的标准支持向量机(SVM)加以改进，提出了一种新的用于回归估计的支持向量机学习算法。
Based on the traditional support vector machine (SVM) for regression, a new learning algorithm of the improved SVM for regression is presented in this paper.
实验表明SVM在稳定性上优于BP网络，而且在对极低密度脂蛋白（VLDL）类别回归估计的精确度上也比BP网络要好。
The experiments show that the proposed SVM-based method is better than the BP network based method in stability and accuracy, especially for the measurements of the VLDL cholesterol levels.
实验表明SVM在稳定性上优于BP网络，而且在对极低密度脂蛋白（VLDL）类别回归估计的精确度上也比BP网络要好。
The experiments show that the proposed SVM-based method is better than the BP network based method in stability and accuracy, especially for the measurements of the VLDL cholesterol levels.
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