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.
提出一种基于关联向量回归模型的盲图像复原算法。
This paper proposed a new blind image restoration algorithm based on relevance vector regression (RVR) models.
论文研究用支持向量回归机处理光顺曲线的重构问题。
How to use SVM to solve the problem of curve smoothing reconstruction in reverse engineering is discussed in this paper.
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.
针对这一问题，提出了支持向量回归多参数的同时调节模型。
To solve this problem, we propose a simultaneous tuning model for multiple parameters of SVR.
提出一种基于支持向量回归的预测驾驶座椅主观舒适度的方法。
The SVR developed and validated using data collected from 12 subjects, and the subjects evaluated five different driving seats.
采用支持向量回归在线辨识算法作为建模方法建立被控对象的逆模型。
Online identification algorithm of support vector regression is used to build the inverse model for the plant.
以支持向量回归为主要算法，讨论了圆锥螺纹各参数的图像检测方法。
The method of the conical thread image detection based on the support vector regression is presented.
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.
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.
通过线性规划技术和采用尺度函数作为核函数来实现支持向量回归模型。
Using linear programming technique and scaling kernel function, the support vector regression model was obtained.
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.
提出一种新的尺度核支持向量回归方法，并应用于非线性系统辨识问题。
A new scaling kernel support vector regression was proposed for nonlinear system identification problem.
本文将基于支持向量回归的数据挖掘方法，用于服务备件需求预测研究中。
This paper applies a new data mining method based on SVR (support vector regression) in the prediction of the spare parts requirement.
并结合实例，讨论了支持向量回归在供应链管理绩效评价中的应用及其特点。
And combined with the example, discussing the application and features of SVR in evaluation of supply chain management performance.
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.
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).
结果表明，支持向量回归具有更强的学习能力，使转换语音具有更好的目标倾向性。
Results show that support vector regression is superior in learning, and make the converted voice more inclined to the target.
利用支持向量回归的方法对非线性过程进行建模，采用预测函数控制方法进行控制。
The support vector regression method is used for modeling the nonlinear process, and the predictive functional control method is used to control.
并结合实例，讨论了支持向量回归在汽车维修服务备件需求预测中的应用及其特点。
And combining with the example, discusses the application and features of SVR in the prediction of the spare parts requirement.
估算结果证明了这种改进的支持向量回归算法在集装箱吞吐量预测中的有效性和实用性。
The results of experiments reveal the practicability and effectiveness of this algorithm in prediction of container throughput.
并且分别详尽的阐述了支持向量分类和支持向量回归的理论思想、计算步骤和优化算法。
It also elaborates the ideas, counting steps and optimize algorithm of support vector classification and 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.
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.
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。
The Support Vector Regression(SVR)is used for the time series analysis and prediction to resolve the complex nonlinear system modeling problems.
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.
同时对广泛的支持向量回归模型、优化支持向量模型的泛化能力和运算速度等方面进行讨论。
The generalization of the support vector regression model, the optimization of the generalization capacity, and the training speed are discussed.
第一阶段称为所谓的资料预先处理，即使用支援向量回归来找出训练资料集中的离异点并删除之。
In the stage I, called as data preprocessing, the support vector machines for regression (SVMR) approach is used to filter out the outliers in the training data set.
第一阶段称为所谓的资料预先处理，即使用支援向量回归来找出训练资料集中的离异点并删除之。
In the stage I, called as data preprocessing, the support vector machines for regression (SVMR) approach is used to filter out the outliers in the training data set.
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