This paper mainly studies the nonparametric regression estimation model of local polynomial.
论文主要研究了非参数局部多项式回归估计模型。
The experimental results show that the proposed SORR algorithm is better than the normal regression estimation algorithm of SVM.
结果表明SORR优于标准的支持向量机回归估计算法。
It was proven theoretically that the estimate errors of unbiased regression estimation are less than that of general regression estimation.
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。
Firstly, we introduce the research situation of the local polynomial regression estimation and its basic concept from the model's background.
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。
Fan J and Gijbels I gave the asymptotic normality of local polynomial regression estimation in dependent time series, where the weighted function is bounded.
对相依时间序列数据，在一定的条件下已有人证明了局部多项式加权回归系数估计服从渐近正态分布，其中核函数是有界的。
SVM is the hot issue accompanying artificial neural network in machine learning. It involves any practical problems such as classification and regression estimation.
支持向量机是继神经网络后机器学习的热点研究技术，它主要应用于分类和回归问题中。
SVM is a kind of general learning algorithms, which has been widely used in pattern recognition, regression estimation, function approximation, density estimation, etc.
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。
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.
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.
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。
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