当然，这样又回归问题本身了，缘何分数就偏低呢？
将遗传程序设计应用于符号回归问题，获得满意的结果。
Applying Genetic Programming to symbolic regression problems, good results were obtained.
支持向量回归机是求解回归问题的新的十分有效的方法。
The support vector machine (SVM) is a very effective method for regression issue.
因此，形成了测试档案，以测试新的功能并且避免现有代码的回归问题。
Therefore, there was an archive of tests to validate new features and to avoid regression problems with existing code.
这种方法被深入地研究并广泛应用在诸如分类和回归问题上。
It has been well studied and widely applied to the classification and regression.
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。
For one linear regression problem in statistical method, the method of modeling is described in this paper.
贝叶斯方法是那些明确地在分类和回归问题中应用贝叶斯定理的算法。
Bayesian methods are those that are explicitly apply Bayes' Theorem for problems such as classification and regression.
基于支持向量机的大样本回归问题一直是一个非常具有挑战性的课题。
It is a very challenging work to deal with large regression problems based on support vector machines.
决策树和决策树的组合，是解决分类问题和回归问题比较流行的一类算法。
Decision trees and their ensembles are popular methods for the machine learning tasks of classification and regression.
内核方法和支持向量机(SVMs)与高斯过程相关并应用于分类和回归问题。
Kernal methods and support Vector Machines (SVMs) are related to Gaussian processes and can also be used in classification and regression problems.
影响分析是研究回归问题的一个重要环节，提到影响分析，必须引进帽子矩阵。
It is known that Hat Matrix is very important in the influence analysis of the unconstrained regression.
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。
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.
支持向量回归问题的研究，对函数拟合（回归逼近）具有重要的理论和应用意义。
The research on support vector regression has an important theoretical and applicable significance on function regression(re-gression approximation).
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。
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 the hot issue accompanying artificial neural network in machine learning. It involves any practical problems such as classification and regression estimation.
支持向量回归机是一种解决回归问题的重要方法，其预测速度与支持向量的稀疏性成正比。
Support Vector regression is an important kind of method for regression problems. The predicting speed of Support Vector regression is proportional to its sparseness.
支持向量回归机是一种解决回归问题的重要方法，其预测速度与支持向量的稀疏性成正比。
The experiments on several pattern classification problems show that HS-LSSVM has high sparseness while holds good classification performance and its sparse process is fast.
在回归中，目前的研究和应用都限于单输出的情况，而实际中有很多属于多输出回归问题。
In the case of regression, however the researches and applications are limited in single output.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
Support vector machines (SVM) are a kind of novel machine learning methods, based on statistical learning theory, which have been developed for solving classification and regression problems.
支持向量机是一种基于统计学习理论的新颖的机器学习方法，该方法已广泛用于解决分类和回归问题。
Support vector machines (SVM) are a kind of novel machine learning methods based on statistical learning theory, which has been developed to solve classification and regression problems.
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。
A support vector regression method based on classification is presented to solve the nonlinear regression problem with unknown data distribution and mathematical model.
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。
But, the standard SVR algorithm can only solve one-dimensional output variable regression problem, thus restrict its application in back analysis field.
随着电子计算技术的飞速发展和实验技术的不断提高，医学资料中经常出现包含较多自变量的大型回归问题。
With the development of the computer technology and the improvement of the experiment technology, there often are the regression problems which include more independent variables in medical data.
主成分回归以其能够有效的降低维数，克服回归问题中的自变量高度相关而产生的分析困难，而得到广泛的利用。
PCR (principal component regress) that with the characters of reducing dimensions effectively and overcoming the intense relativity between independent variables, is widely used in different fields.
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。
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.
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。
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.
核函数方法关心的是如何把输入数据映射到一个高维度的矢量空间，在这个空间中，某些分类或者回归问题可以较容易地解决。
Kernel Methods are concerned with mapping input data into a higher dimensional vector space where some classification or regression problems are easier to model.
核函数方法关心的是如何把输入数据映射到一个高维度的矢量空间，在这个空间中，某些分类或者回归问题可以较容易地解决。
Kernel Methods are concerned with mapping input data into a higher dimensional vector space where some classification or regression problems are easier to model.
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