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.
结果表明，支持向量机回归和预测的最大相对误差不超过6 5%。
The results show that the maximum regression and prediction relative errors are not greater than 6.5%.
本文在国内首次提出将支持向量机回归理论应用到地下矿泉水质参数的预测中。
In this paper, we studied SVM as regression techniques for natural mineral water quality parameters prediction.
提出一种基于支持向量机回归的学习机，并将其应用于单片机智能传感器系统中。
A learning machine based on support vector machine is proposed in this paper, which is applied to single-chip smart sensor system.
现有最小二乘支持向量机回归的训练和模型输出的计算需要较长的时间，不适合在线实时训练。
Least square support vector machines regression without sparsity needs longer training time currently, and is not adapted to online real-time training.
为了更准确的预测油藏四个区域的物性参数，本文提出了结合粗糙集属性约简和支持向量机回归的方法。
To predict reservoir characteristic parameters of four regions exactly, a method based on the attribute reduction by the rough set and SVR is presented.
最小二乘支持向量机回归预测对训练样本数据区间内的预测精度很高，但是对前向外推预测效果不是很好；
RBF neural network is applied to time series forecast with the same data in order to compare the forecast effect with LS-SVM model.
结果显示，把最小二乘支持向量机回归预测与等步长时序预测相结合的预测方法应用于地下工程围岩位移监测数据的分析及预测是可行的；
Combining the advantages of regression analysis methods and time series forecast model with equal step length, a compound forecasting model was set up , and was tested with engineering data.
然后以实测资料对所建模型进行检验，研究结果表明，支持向量机回归模型性能良好、预测精度高、简便易行，是水质评价的一种有效方法，具有广阔的应用前景。
The researching result reveals that SVM regression model presents excellent performance, high prediction accuracy and is easy to run. As a result, it is an effective way and has wide …
支持向量机是一种基于结构风险最小化原理的学习技术，也是一种新的具有很好泛化性能的回归方法。
Support vector machine is a learning technique based on the structural risk minimization principle as well as a new regression method with good generalization ability.
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。
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).
回归型支持向量机方法SVR具有很好的学习性能。
Support Vector Machine for regression (SVR) has shown very good learning performance.
介绍了支持向量机分类和回归算法，将其应用于梁结构的损伤诊断中。
This paper introduces the support vector classification and regression algorithms, which are applied to the structure damage identification.
提出了一种基于支持向量回归机(SVR)的三轴磁通门传感器误差修正方法。
An error correction method for three axial fluxgate sensor based on support vector regression (SVR) is proposed.
提出一种基于支持向量回归机的说话者确认方法。
A speaker verification system based on support vector regression machine (SVR) is presented in this paper.
本文主要对线性支持向量顺序回归机进行理论研究。
For linear support vector ordinal regression machines, some theoretical aspects are studied in this paper.
提出一种基于支持向量回归机(SVR)的非线性动态系统建模方法。
A modeling method for nonlinear dynamic system based on Support Vector Regression (SVR) was proposed in this paper.
同时针对神经网络易于陷入局部极值、结构难以确定和泛化能力较差的缺点，引入了能很好解决小样本、非线性和高维数问题的支持向量回归机来进行油气田开发指标的预测；
The method of support vector regression which can well resolve the problem with the insufficient swatch, nonlinear and high dimension is introduction to predict the development index of gas-field.
为了解决这个问题，本文提出了一种基于特征加权的支持向量回归机。
In order to solve the problem, support vector machine based on weighted feature is proposed in this paper.
支持向量机是一种优秀的学习方法，也是具有很好泛化性能的回归方法。
Support Vector Machine is an excellent learning technique, and it is also a class of regression method with a good generalization ability.
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.
在回归支持向量机的建模中，参数调节问题一直是影响模型性能的重要因素之一。
Parameter tuning of Support Vector Regression (SVR) has been a critical task to develop a SVR model with good generalization performance.
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.
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.
将改进的支持向量回归机与B -样条网络相结合，提出了一种建立回归曲线模型的新算法。
A new algorithm for modeling regression curve is put forward in the paper, it combines B-spline network with improved support vector regression.
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.
比较分析了最小二乘支持向量机（LSSVM）和广义回归神经网络（GRNN）这两种方法的特点。
The features of two methods, i. e. least square support vector machine (LSSVM) and generalized regression neural network (GRNN) are compared and analyzed.
给出带有模糊决策的模糊机会约束规划模型，在此基础上，研究模糊线性支持向量分类机(算法)和模糊线性支持向量回归机(算法)。
Proposed the model of fuzzy chance constrained programming with fuzzy decision, and did some research on fuzzy linear support vector regression (algorithm) on this base.
给出带有模糊决策的模糊机会约束规划模型，在此基础上，研究模糊线性支持向量分类机(算法)和模糊线性支持向量回归机(算法)。
Proposed the model of fuzzy chance constrained programming with fuzzy decision, and did some research on fuzzy linear support vector regression (algorithm) on this base.
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