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.
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.
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.
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%.
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.
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.
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.
当sVM用于回归分析和预测时，通常称其为支持向量回归机svr。
When applied to regression and prediction, we often call SVM as support vector regression machine SVR.
支持向量机(SVM)是一种线性机器，广泛用于模式分类和非线性回归。
The support vector machine (SVM) is a linear classification machine, it is used commonly in the pattern recognition and nonlinear regression.
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.
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.
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.
一种新的自适应支持向量回归神经网络(SVR - NN)提出，它结合了分别支持向量机和神经网络的优点。
A novel adaptive support vector regression neural network (SVR-NN) is proposed, which combines respectively merits of support vector machines and a neural network.
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.
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.
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.
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