The LS-estimator in nonlinear regression problems is an important statistical estimator problem.
非线性回归的LS估计问题是一类重要的统计问题。
For one linear regression problem in statistical method, the method of modeling is described in this paper.
针对统计方法中一元线性回归问题，详细描述了其建模方法。
As new technology of data mining, support vector machines (SVM) have been successfully applied in pattern recognition and regression problem, et al.
支持向量机作为数据挖掘的一项新技术，应用于模式识别和处理回归问题等诸多领域。
But, the standard SVR algorithm can only solve one-dimensional output variable regression problem, thus restrict its application in back analysis field.
但现阶段标准的SVR算法只能解决一维输出变量的回归问题，这就使其在反分析领域的应用受到限制。
Besides, the LQ theorem presented in this paper can be used to change a nonlinear single regression problem to a linear one by means of transformation of variables.
此外，本文提出的LQ定理使我们能用相关分析法，通过变量变换，把单因素非线性回归问题，化成线性形成来处理。
A support vector regression method based on classification is presented to solve the nonlinear regression problem with unknown data distribution and mathematical model.
提出了一种基于分类技术的支持向量回归方法，解决数据分布未知、数学模型未知的非线性回归问题。
Support vector machine (SVM) is an effective method for resolving regression problem, however, traditional SVM is very sensitive to noises and outliers in the training sample.
支持向量机(SVM)是解决回归问题的一种有效的方法，但传统的支持向量机对样本中的噪声和孤立点非常敏感。
Because the separation for GISE is an almost singular regression problem, the classic least square method and principal component method are not satisfactorily for solving the problem.
由于工具误差分离存在着很强的复共线性，传统的最小二乘方法与主成分方法不能很好的解决这一问题。
If you find many defects or regressions, you may choose to alter the regression suite or add more tests, based on those problem areas.
如果你发现很多缺陷或回归，你可以选择改变回归集或加入更多测试，基于那些问题区域。
So it's not a problem, computationally, to create a powerful regression model from a lot of data.
所以，即使要处理的是具有大量数据的功能强大的回归模型，就计算而言，也不是什么问题。
for the most part they'll tell you that they did but the problem is this could be a statistical byproduct of what's called "regression to the mean."
大部分人会说好起来了,但问题是，这很可能是一个所谓,“趋均数回归效应“的统计副产物
This is due to a regression that was introduced when fixing a different problem. It is related to chunked Transfer-Encoding responses from the server.
这是由于回归了固定一个不同的问题时。它是分块传输编码的响应从服务器。
However, we find an interest problem that the regression result is not consistent with the traditional deduction but consistent with the correctional deduction we put forward.
但同时我们发现一个有趣的现象，回归结果与传统理论推论恰恰相反，而与笔者提出的修正推论一致。
To counter the problem of multiple linear regression in statistical forecast, we put forward the method that designs computer software and makes it come true.
本文针对统计预测中的多元线性回归问题，提出了计算机软件的设计与实现方法。
Aiming at the problem of difficult system identification modeling for control system, an identification modeling system was designed for control system by using support vector regression (SVR).
针对非线性控制系统辨识建模较为困难的问题，利用回归型支持向量机(SVR)设计了一例控制系统的辨识建模系统。
Engineering applications, the interpolation and regression (curve fitting) function expression usually are used to describe data to solve the problem.
工程应用中通常用插值和回归(曲线拟合)解决函数表达式描述数据的问题。
This paper deals with the problem of simple substitution for the mixed estimator of mixed regression model and presents some formulas for its influence on estimator precision.
探讨了对混合回归模型的混合估计进行简单替代的问题，并给出了由此产生的对估计精度的影响的表达式。
A way to solve this problem is to perform logistic regression.
解决此问题的一种方法是进行逻辑回归。
It is an inevitable problem to set up a predict equation of discrete variables regression in the practice work.
在实际工作中，建立离散变量的回归预报方程是一个不可回避的问题。
To resolve the problem in the application of the models, the author expanded the classical factor set of models by introducing the concept of integral regression.
为解决模型实际应用中的问题，笔者引用了积分回归的概念，对传统模型的因子集进行了扩充。
This article researches the coefficient estimating problem of the linear regression model and the math analysis foundation of the least squares estimation applying in the coefficient estimating.
本文研究了线性回归模型中的参数估计问题，运用最小二乘法进行参数估计的数学分析基础。
Aimed at solving the challenging problem of diagnosis for sensor bias and drift faults, a novel approach of sensor fault diagnosis based on generalized regression neural network (GRNN) is proposed.
针对诊断传感器偏置故障与漂移故障的难点问题，提出了一种基于广义回归神经网络(GRNN)的传感器故障诊断方法。
As in ordinary regression models, the problem of the heteroscedasticity test still exists in nonlinear models with correlated errors, but, the test for correlation also needs to be considered.
和普通的非线性回归模型一样，具有相关误差的非线性模型也存在异方差检验问题，但通常还要检验相关性。
Correlation and regression analysis showed that the main influencing factors on depression of unemployed were self-accusation, solving problem and the total scores of life events.
相关及回归分析表明：自责、解决问题和生活事件总分是影响下岗职工抑郁的主要因素。
There are also categories that have the same name that describes the problem and the class of algorithm such as Regression and Clustering.
也有一些算法的名字既描述了它处置的问题，也是某一类算法的名称，好比回归和聚类。
The recognition problem is taken as one of classifying among multiple linear regression models, and sparse signal representation is used to solve this problem.
将识别问题看作是多个线性回归模型中的分类问题，并用稀疏表示理论解决这些问题。
This paper discusses the modern regression techniques used for ordinal data analysis and how to apply them into solving an important sampling problem in Consumer Confidence Index survey.
本文讨论了对定序变量进行回归的技术，并探讨了如何用其解决消费者信心指数调查中抽样的重要问题。
Problems of regression function estimate is a basic problem in statistical theory.
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.
回归估计是统计学中基本问题之一。
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