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- Keywords Support Vector Regression（SVR）;Evolutionary Strategy（ES）;SVR parameter tuning;neural networks;predictive modeling;sulphur content prediction; 回归支持向量机;进化策略;参数调节;神经网络;预测模型;含硫量预测;
- A modeling method for nonlinear dynamic system based on Support Vector Regression (SVR) was proposed 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 new algorithm to solve classification problems is obtained based on the support vector regression (SVR). 摘要本文提出了基于支持向量回归机(SVR)的一种新分类算法。
- Support Vector Regression (SVR) algorithm has been introduced to construct a content-based recommend approach. 支持向量机是一种强大的分类工具，由其衍生出的支持向量机回归方法能很好地解决非线性回归问题。
- An digital image watermarking algorithm based on support vector regression (SVR) is proposed in this paper. 摘要提出一种支持向量回归机SVR的数字图像水印算法。
- The Support Vector Regression(SVR) is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems. 摘要 用支持向量回归（SVR）的方法分析和预测时间序列，可解决复杂非线性系统的建模问题。
- Keywords support vector regressor（SVR）;partial least squares（PLS）;acrylonitrile;soft sensing; 支持向量回归机;偏最小二乘法;丙烯腈;软测量;
- A prediction modelling method was proposed for non-linear and non-stationary time series,based on empirical mode decomposition(EMD) and support vector regression(SVR). 提出了一种基于经验模式分解和支持向量回归的非线性、非平稳时间序列预测建模方法。
- The calibration models were built by using support vector regression (SVR).This method can be used to determine vitamin B1, B2 and B6 in synthetic samples. 摘要用支持向量回归方法(SVR)建立校正模型，并对维生素B1、B2和B6的模拟样品进行测定。
- A sensor fault diagnosis system with support vector regression (SVR) for a given turbofan engine was designed.It can detect sensor fault and recover fault-sensor signal. 利用基于回归型支持向量机(SVR)的诊断方法，设计了某型涡扇发动机传感器常见故障诊断系统，实现了传感器故障隔离与信号重构。
- Based on the flight data recorded by FDR equipped on a certain aircraft,the model of inertial phase of aero-engine vibration is established by Support Vector Regression(SVR). 基于某型飞机FDR记录的飞行数据,采用支持向量回归(SVR)方法,建立了航空发动机振动的初始状态辨识模型。
- In this study, we propose a novel application of the Support Vector Regression (SVR) method to model a task variable in the Task-Technology Fit (TTF) theory. 摘要本研究提出一个创新的支持矢量回归方法来探讨应用任务科技适配理论于信息系统采用之问题。
- Support vector regression (SVR), a powerful machine learning technology based on statistical learning theory (SLT) was applied to QSAR on the aldose reductase inhibitory activity of 48 flavones. 用支持向量机回归(SVR)方法研究了48个黄酮类化合物醛糖还原酶抑制活性的定量构效关系。
- A brief introduction on the basic principle of support vector machine (SVM) is given herein, and then a safety monitoring model for earth-rockfill dam is put forward based on support vector regression (SVR). 摘要简要介绍了支持向量机的基本原理，在对建模因子进行分析的基础上，提出了基于支持向量回归(SVR)算法的土石坝安全监测预报模型。
- As one of the promising learning machines, parameters selection is one of the key issues in the Support Vector Machine (SVM), specifically the Support Vector Regression (SVR), learning method. 作为一种具有很好应用前景的学习机器,支持向量机(SVM)的超参数选择在实际应用中一直是一个十分重要的问题。
- In order to improve support vector regression (SVR) learning ability and generalization performance, a joint optimization method for selecting features and SVR parameters is proposed. 摘要 为提高支持向量回归算法的学习能力和泛化性能，提出了特征选择和支持向量回归参数的联合优化方法。
- The support vector regression( SVR) is introduced into the inverse control system to propose a kind of adaptive inverse control method based on support vector regression( SVR-AIC). 将支持向量回归引入逆控制,提出了一种基于支持向量回归的自适应逆控制方法。
- Inspired by the advantage of local learning and support vector regression(SVR) dealing with finite data,locally weighted SVR(LW-SVR) is adopted to implement the case-based learning. 由于支持向量回归(SVR)在处理小样本问题的优越性并受局部学习思想的启发,采用局部加权SVR(LW-SVR)实现案例学习。
- Considering the favorable performance of support vector machines (SVM) in the respect of machine learning, a seepage-monitoring model based on support vector regression (SVR) is put forward. 鉴于支持向量机在机器学习方面表现出的良好性能,提出了基于支持向量回归(SVR)算法的贮灰坝渗流监测模型。
- Support Vector Regression ( for short SVR ) is an important branch of SVM, SVR has been applied to system identification, nonlinear system prediction and good results have been demonstrated. 支持向量函数回归(简称SVR)是SVM的一个重要分支，它已经成功的应用于系统识别、非线性系统的预测等方面，并取得了较好的效果。
